EfficientDNNs

A collection of recent methods on DNN compression and acceleration. There are mainly 5 kinds of methods for efficient DNNs:

• neural architecture re-designing or searching
  • maintain accuracy, less cost (e.g., #Params, #FLOPs, etc.): MobileNet, ShuffleNet etc.
  • maintain cost, more accuracy: Inception, ResNeXt, Xception etc.
• pruning (including structured and unstructured)
• quantization
• matrix decomposition
• knowledge distillation

About abbreviation: In the list below, o for oral, w for workshop, s for spotlight, b for best paper.

Papers

• 2011-JMLR-Learning with Structured Sparsity
• 2011-NIPSw-Improving the speed of neural networks on CPUs
• 2013-NIPS-Predicting Parameters in Deep Learning
• 2013.08-Estimating or Propagating Gradients Through Stochastic Neurons for Conditional Computation
• 2014-BMVC-Speeding up convolutional neural networks with low rank expansions
• 2014-INTERSPEECH-1-Bit Stochastic Gradient Descent and its Application to Data-Parallel Distributed Training of Speech DNNs
• 2014-NIPS-Exploiting Linear Structure Within Convolutional Networks for Efficient Evaluation
• 2014-NIPS-Do deep neural nets really need to be deep
• 2014.12-Memory bounded deep convolutional networks
• 2015-ICLR-Speeding-up convolutional neural networks using fine-tuned cp-decomposition
• 2015-ICML-Compressing neural networks with the hashing trick
• 2015-INTERSPEECH-A Diversity-Penalizing Ensemble Training Method for Deep Learning
• 2015-BMVC-Data-free parameter pruning for deep neural networks
• 2015-BMVC-Learning the structure of deep architectures using l1 regularization
• 2015-NIPS-Learning both Weights and Connections for Efficient Neural Network
• 2015-NIPS-Binaryconnect: Training deep neural networks with binary weights during propagations
• 2015-NIPS-Structured Transforms for Small-Footprint Deep Learning
• 2015-NIPS-Tensorizing Neural Networks
• 2015-NIPSw-Distilling Intractable Generative Models
• 2015-NIPSw-Federated Optimization:Distributed Optimization Beyond the Datacenter
• 2015-CVPR-Efficient and Accurate Approximations of Nonlinear Convolutional Networks [2016 TPAMI version: Accelerating Very Deep Convolutional Networks for Classification and Detection]
• 2015-CVPR-Sparse Convolutional Neural Networks
• 2015-ICCV-An Exploration of Parameter Redundancy in Deep Networks with Circulant Projections
• 2015.12-Exploiting Local Structures with the Kronecker Layer in Convolutional Networks
• 2016-ICLR-Deep Compression: Compressing Deep Neural Networks with Pruning, Trained Quantization and Huffman Coding (best paper!)
• 2016-ICLR-All you need is a good init [Code]
• 2016-ICLR-Convolutional neural networks with low-rank regularization [Code]
• 2016-ICLR-Diversity networks
• 2016-ICLR-Neural networks with few multiplications
• 2016-ICLR-Compression of deep convolutional neural networks for fast and low power mobile applications
• 2016-ICLRw-Randomout: Using a convolutional gradient norm to win the filter lottery
• 2016-CVPR-Fast algorithms for convolutional neural networks
• 2016-CVPR-Fast ConvNets Using Group-wise Brain Damage
• 2016-BMVC-Learning neural network architectures using backpropagation
• 2016-ECCV-Less is more: Towards compact cnns
• 2016-EMNLP-Sequence-Level Knowledge Distillation
• 2016-NIPS-Learning Structured Sparsity in Deep Neural Networks
• 2016-NIPS-Dynamic Network Surgery for Efficient DNNs [Code]
• 2016-NIPS-Learning the Number of Neurons in Deep Neural Networks
• 2016-NIPS-Memory-Efficient Backpropagation Through Time
• 2016-NIPS-PerforatedCNNs: Acceleration through Elimination of Redundant Convolutions
• 2016-NIPS-LightRNN: Memory and Computation-Efficient Recurrent Neural Networks
• 2016-NIPS-CNNpack: packing convolutional neural networks in the frequency domain
• 2016-NIPSw-Federated Learning: Strategies for Improving Communication Efficiency
• 2016-ISCA-Eyeriss: A Spatial Architecture for Energy-Efficient Dataflow for Convolutional Neural Networks
• 2016-ICASSP-Learning compact recurrent neural networks
• 2016-CoNLL-Compression of Neural Machine Translation Models via Pruning
• 2016.03-Adaptive Computation Time for Recurrent Neural Networks
• 2016.06-Structured Convolution Matrices for Energy-efficient Deep learning
• 2016.06-Deep neural networks are robust to weight binarization and other non-linear distortions
• 2016.06-Hypernetworks
• 2016.07-IHT-Training skinny deep neural networks with iterative hard thresholding methods
• 2016.08-Recurrent Neural Networks With Limited Numerical Precision
• 2016.10-Deep model compression: Distilling knowledge from noisy teachers
• 2016.10-Federated Optimization: Distributed Machine Learning for On-Device Intelligence
• 2017-ICLR-Pruning Convolutional Neural Networks for Resource Efficient Inference
• 2017-ICLR-Incremental Network Quantization: Towards Lossless CNNs with Low-Precision Weights [Code]
• 2017-ICLR-Do Deep Convolutional Nets Really Need to be Deep and Convolutional?
• 2017-ICLR-DSD: Dense-Sparse-Dense Training for Deep Neural Networks (Closely related work: SFP and IHT)
• 2017-ICLR-Faster CNNs with Direct Sparse Convolutions and Guided Pruning
• 2017-ICLR-Towards the Limit of Network Quantization
• 2017-ICLR-Loss-aware Binarization of Deep Networks
• 2017-ICLR-Trained Ternary Quantization [Code]
• 2017-ICLR-Exploring Sparsity in Recurrent Neural Networks
• 2017-ICLR-Soft Weight-Sharing for Neural Network Compression [Reddit discussion]
• 2017-ICLR-Variable Computation in Recurrent Neural Networks
• 2017-ICLR-Training Compressed Fully-Connected Networks with a Density-Diversity Penalty
• 2017-ICML-Variational dropout sparsifies deep neural networks
• 2017-ICML-Theoretical Properties for Neural Networks with Weight Matrices of Low Displacement Rank
• 2017-ICML-Deep Tensor Convolution on Multicores
• 2017-ICML-Delta Networks for Optimized Recurrent Network Computation
• 2017-ICML-Beyond Filters: Compact Feature Map for Portable Deep Model
• 2017-ICML-Combined Group and Exclusive Sparsity for Deep Neural Networks
• 2017-ICML-MEC: Memory-efficient Convolution for Deep Neural Network
• 2017-ICML-Deciding How to Decide: Dynamic Routing in Artificial Neural Networks
• 2017-ICML-ZipML: Training Models with End-to-End Low Precision: The Cans, the Cannots, and a Little Bit of Deep Learning
• 2017-ICML-Analytical Guarantees on Numerical Precision of Deep Neural Networks
• 2017-ICML-Adaptive Neural Networks for Efficient Inference
• 2017-ICML-SplitNet: Learning to Semantically Split Deep Networks for Parameter Reduction and Model Parallelization
• 2017-ICMLw-Bayesian Sparsification of Recurrent Neural Networks
• 2017-CVPR-Learning deep CNN denoiser prior for image restoration
• 2017-CVPR-Deep roots: Improving cnn efficiency with hierarchical filter groups
• 2017-CVPR-More is less: A more complicated network with less inference complexity
• 2017-CVPR-All You Need is Beyond a Good Init: Exploring Better Solution for Training Extremely Deep Convolutional Neural Networks with Orthonormality and Modulation
• 2017-CVPR-ResNeXt-Aggregated Residual Transformations for Deep Neural Networks
• 2017-CVPR-Xception: Deep learning with depthwise separable convolutions
• 2017-CVPR-Designing Energy-Efficient CNN using Energy-aware Pruning
• 2017-CVPR-Spatially Adaptive Computation Time for Residual Networks
• 2017-CVPR-Network Sketching: Exploiting Binary Structure in Deep CNNs
• 2017-CVPR-A Compact DNN: Approaching GoogLeNet-Level Accuracy of Classification and Domain Adaptation
• 2017-ICCV-Channel pruning for accelerating very deep neural networks [Code]
• 2017-ICCV-Learning efficient convolutional networks through network slimming [Code]
• 2017-ICCV-ThiNet: A filter level pruning method for deep neural network compression [Project] [Code] [2018 TPAMI version]
• 2017-ICCV-Interleaved group convolutions
• 2017-ICCV-Coordinating Filters for Faster Deep Neural Networks [Code]
• 2017-ICCV-Performance Guaranteed Network Acceleration via High-Order Residual Quantization
• 2017-NIPS-Net-trim: Convex pruning of deep neural networks with performance guarantee [Code]
• 2017-NIPS-Runtime neural pruning
• 2017-NIPS-Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon [Code]
• 2017-NIPS-Federated Multi-Task Learning
• 2017-NIPS-Bayesian Compression for Deep Learning
• 2017-NIPS-Structured Bayesian Pruning via Log-Normal Multiplicative Noise
• 2017-NIPS-Towards Accurate Binary Convolutional Neural Network
• 2017-NIPS-Soft-to-Hard Vector Quantization for End-to-End Learning Compressible Representations
• 2017-NIPS-TernGrad: Ternary Gradients to Reduce Communication in Distributed Deep Learning
• 2017-NIPS-Flexpoint: An Adaptive Numerical Format for Efficient Training of Deep Neural Networks
• 2017-NIPS-Training Quantized Nets: A Deeper Understanding
• 2017-NIPS-The Reversible Residual Network: Backpropagation Without Storing Activations [Code]
• 2017-NIPS-Compression-aware Training of Deep Networks
• 2017-FPGA-ESE: efficient speech recognition engine with compressed LSTM on FPGA (best paper!)
• 2017-AISTATS-Communication-Efficient Learning of Deep Networks from Decentralized Data
• 2017-ICASSP-Accelerating Deep Convolutional Networks using low-precision and sparsity
• 2017-NNs-Nonredundant sparse feature extraction using autoencoders with receptive fields clustering
• 2017.02-The Power of Sparsity in Convolutional Neural Networks
• 2017.07-Stochastic, Distributed and Federated Optimization for Machine Learning
• 2017.05-Structural Compression of Convolutional Neural Networks Based on Greedy Filter Pruning
• 2017.07-Extremely Low Bit Neural Network: Squeeze the Last Bit Out with ADMM
• 2017.11-GPU Kernels for Block-Sparse Weights [Code] (OpenAI)
• 2017.11-Block-sparse recurrent neural networks (Baidu)
• 2018-AAAI-Auto-balanced Filter Pruning for Efficient Convolutional Neural Networks
• 2018-AAAI-Deep Neural Network Compression with Single and Multiple Level Quantization
• 2018-AAAI-Dynamic Deep Neural Networks_Optimizing Accuracy-Efficiency Trade-offs by Selective Execution
• 2018-ICML-On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization
• 2018-ICML-Weightless: Lossy Weight Encoding For Deep Neural Network Compression
• 2018-ICMLw-Assessing the Scalability of Biologically-Motivated Deep Learning Algorithms and Architectures
• 2018-ICML-Understanding and simplifying one-shot architecture search
• 2018-ICLRo-Training and Inference with Integers in Deep Neural Networks
• 2018-ICLR-Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers
• 2018-ICLR-N2N learning: Network to Network Compression via Policy Gradient Reinforcement Learning
• 2018-ICLR-Model compression via distillation and quantization
• 2018-ICLR-Towards Image Understanding from Deep Compression Without Decoding
• 2018-ICLR-Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training
• 2018-ICLR-Mixed Precision Training of Convolutional Neural Networks using Integer Operations
• 2018-ICLR-Mixed Precision Training
• 2018-ICLR-Apprentice: Using Knowledge Distillation Techniques To Improve Low-Precision Network Accuracy
• 2018-ICLR-Loss-aware Weight Quantization of Deep Networks
• 2018-ICLR-Alternating Multi-bit Quantization for Recurrent Neural Networks
• 2018-ICLR-Adaptive Quantization of Neural Networks
• 2018-ICLR-Variational Network Quantization
• 2018-ICLR-Espresso: Efficient Forward Propagation for Binary Deep Neural Networks
• 2018-ICLR-Learning to share: Simultaneous parameter tying and sparsification in deep learning
• 2018-ICLR-Learning Sparse Neural Networks through L0 Regularization
• 2018-ICLR-WRPN: Wide Reduced-Precision Networks
• 2018-ICLR-Deep rewiring: Training very sparse deep networks
• 2018-ICLR-Efficient sparse-winograd convolutional neural networks [Code]
• 2018-ICLR-Learning Intrinsic Sparse Structures within Long Short-term Memory
• 2018-ICLR-Multi-scale dense networks for resource efficient image classification
• 2018-ICLR-Compressing Word Embedding via Deep Compositional Code Learning
• 2018-ICLR-Learning Discrete Weights Using the Local Reparameterization Trick
• 2018-ICLR-Training wide residual networks for deployment using a single bit for each weight
• 2018-ICLR-The High-Dimensional Geometry of Binary Neural Networks
• 2018-ICLRw-To Prune, or Not to Prune: Exploring the Efficacy of Pruning for Model Compression (Similar topic: 2018-NIPSw-nip in the bud, 2018-NIPSw-rethink)
• 2018-ICLRw-Systematic Weight Pruning of DNNs using Alternating Direction Method of Multipliers
• 2018-ICLRw-Weightless: Lossy weight encoding for deep neural network compression
• 2018-ICLRw-Variance-based Gradient Compression for Efficient Distributed Deep Learning
• 2018-ICLRw-Stacked Filters Stationary Flow For Hardware-Oriented Acceleration Of Deep Convolutional Neural Networks
• 2018-ICLRw-Training Shallow and Thin Networks for Acceleration via Knowledge Distillation with Conditional Adversarial Networks
• 2018-ICLRw-Accelerating Neural Architecture Search using Performance Prediction
• 2018-ICLRw-Nonlinear Acceleration of CNNs
• 2018-ICLRw-Attention-Based Guided Structured Sparsity of Deep Neural Networks [Code]
• 2018-CVPR-Context-Aware Deep Feature Compression for High-Speed Visual Tracking
• 2018-CVPR-NISP: Pruning Networks using Neuron Importance Score Propagation
• 2018-CVPR-Deep Image Prior [Code]
• 2018-CVPR-Condensenet: An efficient densenet using learned group convolutions [Code]
• 2018-CVPR-Shift: A zero flop, zero parameter alternative to spatial convolutions
• 2018-CVPR-Explicit Loss-Error-Aware Quantization for Low-Bit Deep Neural Networks
• 2018-CVPR-Interleaved structured sparse convolutional neural networks
• 2018-CVPR-Towards Effective Low-bitwidth Convolutional Neural Networks
• 2018-CVPR-CLIP-Q: Deep Network Compression Learning by In-Parallel Pruning-Quantization
• 2018-CVPR-Blockdrop: Dynamic inference paths in residual networks
• 2018-CVPR-Nestednet: Learning nested sparse structures in deep neural networks
• 2018-CVPR-Stochastic downsampling for cost-adjustable inference and improved regularization in convolutional networks
• 2018-CVPR-Wide Compression: Tensor Ring Nets
• 2018-CVPR-Learning Compact Recurrent Neural Networks With Block-Term Tensor Decomposition
• 2018-CVPR-Learning Time/Memory-Efficient Deep Architectures With Budgeted Super Networks
• 2018-CVPR-HydraNets: Specialized Dynamic Architectures for Efficient Inference
• 2018-CVPR-SYQ: Learning Symmetric Quantization for Efficient Deep Neural Networks
• 2018-CVPR-Towards Effective Low-Bitwidth Convolutional Neural Networks
• 2018-CVPR-Two-Step Quantization for Low-Bit Neural Networks
• 2018-CVPR-Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference
• 2018-CVPR-"Learning-Compression" Algorithms for Neural Net Pruning
• 2018-CVPR-PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning [Code]
• 2018-CVPR-MorphNet: Fast & Simple Resource-Constrained Structure Learning of Deep Networks
• 2018-CVPR-ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
• 2018-CVPRw-Squeezenext: Hardware-aware neural network design
• 2018-ICML-Compressing Neural Networks using the Variational Information Bottleneck
• 2018-ICML-DCFNet: Deep Neural Network with Decomposed Convolutional Filters
• 2018-ICML-Deep k-Means Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions
• 2018-ICML-Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization
• 2018-ICML-High Performance Zero-Memory Overhead Direct Convolutions
• 2018-ICML-Kronecker Recurrent Units
• 2018-ICML-Weightless: Lossy weight encoding for deep neural network compression
• 2018-ICML-StrassenNets: Deep learning with a multiplication budget
• 2018-ICML-Learning Compact Neural Networks with Regularization
• 2018-ICML-WSNet: Compact and Efficient Networks Through Weight Sampling
• 2018-ICML-Gradually Updated Neural Networks for Large-Scale Image Recognition [Code]
• 2018-IJCAI-Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error
• 2018-IJCAI-SFP-Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks [Code]
• 2018-IJCAI-Where to Prune: Using LSTM to Guide End-to-end Pruning
• 2018-IJCAI-Accelerating Convolutional Networks via Global & Dynamic Filter Pruning
• 2018-IJCAI-Optimization based Layer-wise Magnitude-based Pruning for DNN Compression
• 2018-IJCAI-Progressive Blockwise Knowledge Distillation for Neural Network Acceleration
• 2018-IJCAI-Complementary Binary Quantization for Joint Multiple Indexing
• 2018-ECCV-A Systematic DNN Weight Pruning Framework using Alternating Direction Method of Multipliers
• 2018-ECCV-Coreset-Based Neural Network Compression
• 2018-ECCV-Data-Driven Sparse Structure Selection for Deep Neural Networks [Code]
• 2018-ECCV-Training Binary Weight Networks via Semi-Binary Decomposition
• 2018-ECCV-Learning Compression from Limited Unlabeled Data
• 2018-ECCV-Constraint-Aware Deep Neural Network Compression
• 2018-ECCV-Sparsely Aggregated Convolutional Networks
• 2018-ECCV-Deep Expander Networks: Efficient Deep Networks from Graph Theory [Code]
• 2018-ECCV-SparseNet-Sparsely Aggregated Convolutional Networks [Code]
• 2018-ECCV-Ask, acquire, and attack: Data-free uap generation using class impressions
• 2018-ECCV-Netadapt: Platform-aware neural network adaptation for mobile applications
• 2018-ECCV-Clustering Convolutional Kernels to Compress Deep Neural Networks
• 2018-ECCV-Bi-Real Net: Enhancing the Performance of 1-bit CNNs With Improved Representational Capability and Advanced Training Algorithm
• 2018-ECCV-Extreme Network Compression via Filter Group Approximation
• 2018-ECCV-Convolutional Networks with Adaptive Inference Graphs
• 2018-ECCV-SkipNet: Learning Dynamic Routing in Convolutional Networks [Code]
• 2018-ECCV-Value-aware Quantization for Training and Inference of Neural Networks
• 2018-ECCV-LQ-Nets: Learned Quantization for Highly Accurate and Compact Deep Neural Networks
• 2018-ECCV-AMC: AutoML for Model Compression and Acceleration on Mobile Devices
• 2018-ECCV-Piggyback: Adapting a single network to multiple tasks by learning to mask weights
• 2018-BMVCo-Structured Probabilistic Pruning for Convolutional Neural Network Acceleration
• 2018-BMVC-Efficient Progressive Neural Architecture Search
• 2018-BMVC-Igcv3: Interleaved lowrank group convolutions for efficient deep neural networks
• 2018-NIPS-Discrimination-aware Channel Pruning for Deep Neural Networks
• 2018-NIPS-Frequency-Domain Dynamic Pruning for Convolutional Neural Networks
• 2018-NIPS-ChannelNets: Compact and Efficient Convolutional Neural Networks via Channel-Wise Convolutions
• 2018-NIPS-DropBlock: A regularization method for convolutional networks
• 2018-NIPS-Constructing fast network through deconstruction of convolution
• 2018-NIPS-Learning Versatile Filters for Efficient Convolutional Neural Networks [Code]
• 2018-NIPS-Moonshine: Distilling with cheap convolutions
• 2018-NIPS-HitNet: hybrid ternary recurrent neural network
• 2018-NIPS-FastGRNN: A Fast, Accurate, Stable and Tiny Kilobyte Sized Gated Recurrent Neural Network
• 2018-NIPS-Training DNNs with Hybrid Block Floating Point
• 2018-NIPS-Reversible Recurrent Neural Networks
• 2018-NIPS-Synaptic Strength For Convolutional Neural Network
• 2018-NIPS-Learning sparse neural networks via sensitivity-driven regularization
• 2018-NIPS-Multi-Task Zipping via Layer-wise Neuron Sharing
• 2018-NIPS-A Linear Speedup Analysis of Distributed Deep Learning with Sparse and Quantized Communication
• 2018-NIPS-Gradient Sparsification for Communication-Efficient Distributed Optimization
• 2018-NIPS-GradiVeQ: Vector Quantization for Bandwidth-Efficient Gradient Aggregation in Distributed CNN Training
• 2018-NIPS-ATOMO: Communication-efficient Learning via Atomic Sparsification
• 2018-NIPS-Norm matters: efficient and accurate normalization schemes in deep networks
• 2018-NIPS-Sparsified SGD with memory
• 2018-NIPS-Pelee: A Real-Time Object Detection System on Mobile Devices
• 2018-NIPS-Scalable methods for 8-bit training of neural networks
• 2018-NIPS-TETRIS: TilE-matching the TRemendous Irregular Sparsity
• 2018-NIPS-Training deep neural networks with 8-bit floating point numbers
• 2018-NIPS-Multiple instance learning for efficient sequential data classification on resource-constrained devices
• 2018-NIPSw-Pruning neural networks: is it time to nip it in the bud?
• 2018-NIPSwb-Rethinking the Value of Network Pruning [2019 ICLR version]
• 2018-NIPSw-Structured Pruning for Efficient ConvNets via Incremental Regularization [2019 IJCNN version] [Code]
• 2018-NIPSw-Adaptive Mixture of Low-Rank Factorizations for Compact Neural Modeling
• 2018-NIPSw-Learning Sparse Networks Using Targeted Dropout [OpenReview] [Code]
• 2018-WACV-Recovering from Random Pruning: On the Plasticity of Deep Convolutional Neural Networks
• 2018.05-Compression of Deep Convolutional Neural Networks under Joint Sparsity Constraints
• 2018.05-AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference
• 2018.10-A Closer Look at Structured Pruning for Neural Network Compression [Code]
• 2018.11-Second-order Optimization Method for Large Mini-batch: Training ResNet-50 on ImageNet in 35 Epochs
• 2018.11-PydMobileNet: Improved Version of MobileNets with Pyramid Depthwise Separable Convolution
• 2019-SysML-Towards Federated Learning at Scale: System Design
• 2019-SysML-To compress or not to compress: Understanding the Interactions between Adversarial Attacks and Neural Network Compression
• 2019-ICLRo-The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks (best paper!)
• 2019-ICLR-Slimmable Neural Networks [Code]
• 2019-ICLR-Defensive Quantization: When Efficiency Meets Robustness
• 2019-ICLR-Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters [Code]
• 2019-ICLR-ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware [Code]
• 2019-ICLR-SNIP: Single-shot Network Pruning based on Connection Sensitivity
• 2019-ICLR-Non-vacuous Generalization Bounds at the ImageNet Scale: a PAC-Bayesian Compression Approach
• 2019-ICLR-Dynamic Channel Pruning: Feature Boosting and Suppression
• 2019-ICLR-Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking
• 2019-ICLR-RotDCF: Decomposition of Convolutional Filters for Rotation-Equivariant Deep Networks
• 2019-ICLR-Dynamic Sparse Graph for Efficient Deep Learning
• 2019-ICLR-Big-Little Net: An Efficient Multi-Scale Feature Representation for Visual and Speech Recognition
• 2019-ICLR-Data-Dependent Coresets for Compressing Neural Networks with Applications to Generalization Bounds
• 2019-ICLR-Learning Recurrent Binary/Ternary Weights
• 2019-ICLR-Double Viterbi: Weight Encoding for High Compression Ratio and Fast On-Chip Reconstruction for Deep Neural Network
• 2019-ICLR-Relaxed Quantization for Discretized Neural Networks
• 2019-ICLR-Integer Networks for Data Compression with Latent-Variable Models
• 2019-ICLR-Minimal Random Code Learning: Getting Bits Back from Compressed Model Parameters
• 2019-ICLR-Analysis of Quantized Models
• 2019-ICLR-DARTS: Differentiable Architecture Search [Code]
• 2019-ICLR-Graph HyperNetworks for Neural Architecture Search
• 2019-ICLR-Learnable Embedding Space for Efficient Neural Architecture Compression [Code]
• 2019-ICLR-Efficient Multi-Objective Neural Architecture Search via Lamarckian Evolution
• 2019-ICLR-SNAS: stochastic neural architecture search (SenseTime)
• 2019-AAAIo-A layer decomposition-recomposition framework for neuron pruning towards accurate lightweight networks
• 2019-AAAI-Balanced Sparsity for Efficient DNN Inference on GPU [Code]
• 2019-AAAI-CircConv: A Structured Convolution with Low Complexity
• 2019-AAAI-Regularized Evolution for Image Classifier Architecture Search
• 2019-AAAI-Universal Approximation Property and Equivalence of Stochastic Computing-Based Neural Networks and Binary Neural Networks
• 2019-ASPLOS-Packing Sparse Convolutional Neural Networks for Efficient Systolic Array Implementations: Column Combining Under Joint Optimization
• 2019-CVPRo-HAQ: hardware-aware automated quantization
• 2019-CVPRo-Filter Pruning via Geometric Median for Deep Convolutional Neural Networks Acceleration [Code]
• 2019-CVPR-All You Need is a Few Shifts: Designing Efficient Convolutional Neural Networks for Image Classification
• 2019-CVPR-Importance Estimation for Neural Network Pruning [Code]
• 2019-CVPR-HetConv Heterogeneous Kernel-Based Convolutions for Deep CNNs
• 2019-CVPR-Fully Learnable Group Convolution for Acceleration of Deep Neural Networks
• 2019-CVPR-Towards Optimal Structured CNN Pruning via Generative Adversarial Learning
• 2019-CVPR-Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure
• 2019-CVPR-Searching for A Robust Neural Architecture in Four GPU Hours [Code]
• 2019-CVPR-FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search
• 2019-CVPR-RENAS: Reinforced Evolutionary Neural Architecture Search
• 2019-CVPR-ChamNet: Towards Efficient Network Design through Platform-Aware Model Adaptation
• 2019-CVPR-Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search [Code]
• 2019-CVPR-Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation [Code]
• 2019-CVPR-MnasNet: Platform-Aware Neural Architecture Search for Mobile [Code]
• 2019-CVPR-MFAS: Multimodal Fusion Architecture Search
• 2019-CVPR-A Neurobiological Evaluation Metric for Neural Network Model Search
• 2019-CVPR-Fast Neural Architecture Search of Compact Semantic Segmentation Models via Auxiliary Cells
• 2019-CVPR-Efficient Neural Network Compression [Code]
• 2019-CVPR-T-Net: Parametrizing Fully Convolutional Nets with a Single High-Order Tensor
• 2019-CVPR-Centripetal SGD for Pruning Very Deep Convolutional Networks with Complicated Structure [Code]
• 2019-CVPR-DSC: Dense-Sparse Convolution for Vectorized Inference of Convolutional Neural Networks
• 2019-CVPR-DupNet: Towards Very Tiny Quantized CNN With Improved Accuracy for Face Detection
• 2019-CVPR-ECC: Platform-Independent Energy-Constrained Deep Neural Network Compression via a Bilinear Regression Model
• 2019-CVPR-Variational Convolutional Neural Network Pruning
• 2019-CVPR-Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization [Code]
• 2019-CVPR-Accelerating Convolutional Neural Networks via Activation Map Compression
• 2019-CVPR-Compressing Convolutional Neural Networks via Factorized Convolutional Filters
• 2019-CVPR-Deep Virtual Networks for Memory Efficient Inference of Multiple Tasks
• 2019-CVPR-Exploiting Kernel Sparsity and Entropy for Interpretable CNN Compression
• 2019-CVPR-MBS: Macroblock Scaling for CNN Model Reduction
• 2019-CVPR-On Implicit Filter Level Sparsity in Convolutional Neural Networks
• 2019-CVPR-Structured Pruning of Neural Networks With Budget-Aware Regularization
• 2019-CVPRo-Neural Rejuvenation: Improving Deep Network Training by Enhancing Computational Resource Utilization [Code]
• 2019-ICML-Approximated Oracle Filter Pruning for Destructive CNN Width Optimization [Code]
• 2019-ICML-EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis [Code]
• 2019-ICML-Zero-Shot Knowledge Distillation in Deep Networks [Code]
• 2019-ICML-LegoNet: Efficient Convolutional Neural Networks with Lego Filters [Code]
• 2019-ICML-EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks [Code]
• 2019-ICML-Collaborative Channel Pruning for Deep Networks
• 2019-ICML-Training CNNs with Selective Allocation of Channels
• 2019-ICML-NAS-Bench-101: Towards Reproducible Neural Architecture Search [Code]
• 2019-ICMLw-Towards Learning of Filter-Level Heterogeneous Compression of Convolutional Neural Networks [Code] (AutoML workshop)
• 2019-IJCAI-Play and Prune: Adaptive Filter Pruning for Deep Model Compression
• 2019-BigComp-Towards Robust Compressed Convolutional Neural Networks
• 2019-ICCV-Rethinking ImageNet Pre-training
• 2019-ICCV-Universally Slimmable Networks and Improved Training Techniques (related: 2019 ICLR Slimmable)
• 2019-ICCV-MetaPruning: Meta Learning for Automatic Neural Network Channel Pruning [Code]
• 2019-ICCV-ACNet: Strengthening the Kernel Skeletons for Powerful CNN via Asymmetric Convolution Blocks
• 2019-NIPS-Global Sparse Momentum SGD for Pruning Very Deep Neural Networks
• 2019-NIPS-Model Compression with Adversarial Robustness: A Unified Optimization Framework
• 2019-NIPS-AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters
• 2019-NIPS-Double Quantization for Communication-Efficient Distributed Optimization
• 2019-NIPS-Focused Quantization for Sparse CNNs
• 2019-NIPS-E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings
• 2019-NIPS-MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization
• 2019-NIPS-Random Projections with Asymmetric Quantization
• 2019-NIPS-Network Pruning via Transformable Architecture Search [Code]
• 2019-NIPS-Point-Voxel CNN for Efficient 3D Deep Learning [Code]
• 2019-NIPS-Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks
• 2019-NIPS-A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off
• 2019-NIPS-Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations
• 2019-NIPS-Post training 4-bit quantization of convolutional networks for rapid-deployment
• 2019-PR-Filter-in-Filter: Improve CNNs in a Low-cost Way by Sharing Parameters among the Sub-filters of a Filter
• 2019-PRL-BDNN: Binary Convolution Neural Networks for Fast Object Detection
• 2019-TNNLS-Towards Compact ConvNets via Structure-Sparsity Regularized Filter Pruning [Code]
• 2019-JMLR-Neural Architecture Search: A Survey
• 2019.03-Network Slimming by Slimmable Networks: Towards One-Shot Architecture Search for Channel Numbers [Code]
• 2019.03-Single Path One-Shot Neural Architecture Search with Uniform Sampling
• 2019.04-Resource Efficient 3D Convolutional Neural Networks
• 2019.04-Meta Filter Pruning to Accelerate Deep Convolutional Neural Networks
• 2019.04-Knowledge Squeezed Adversarial Network Compression
• 2019.04-Progressive Differentiable Architecture Search: Bridging the Depth Gap between Search and Evaluation [Code]
• 2019.05-Dynamic Neural Network Channel Execution for Efficient Training
• 2019.06-AutoGrow: Automatic Layer Growing in Deep Convolutional Networks
• 2019.06-BasisConv: A method for compressed representation and learning in CNNs
• 2019.06-BlockSwap: Fisher-guided Block Substitution for Network Compression
• 2019.06-Data-Free Quantization through Weight Equalization and Bias Correction
• 2019.06-Separable Layers Enable Structured Efficient Linear Substitutions [Code]
• 2019.06-Butterfly Transform: An Efficient FFT Based Neural Architecture Design
• 2019.06-A Taxonomy of Channel Pruning Signals in CNNs
• 2019.08-Adversarial Neural Pruning with Latent Vulnerability Suppression
• 2019.09-Training convolutional neural networks with cheap convolutions and online distillation
• 2019.09-Pruning from Scratch
• 2019.10-Structured Pruning of Large Language Models
• 2019.11-Adversarial Interpolation Training: A Simple Approach for Improving Model Robustness
• 2020-AAAI-Pconv: The missing but desirable sparsity in dnn weight pruning for real-time execution on mobile devices

NAS (Neural Architecture Search)

• 2016.11-Neural architecture search with reinforcement learning
• 2019-NIPS-Meta Architecture Search
• 2019-NIPS-SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers

Papers-Advesarial Attacks

• 2019-CVPR-ComDefend: An Efficient Image Compression Model to Defend Adversarial Examples [Code]

Papers-Visualization and Interpretability

• 2010-JMLR-How to explain individual classification decisions
• 2015-PLOS ONE-On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
• 2015-CVPR-Learning to generate chairs with convolutional neural networks
• 2015-CVPR-Understanding deep image representations by inverting them [2016 IJCV version: Visualizing deep convolutional neural networks using natural pre-images]
• 2016-CVPR-Inverting Visual Representations with Convolutional Networks
• 2016-KDD-"Why Should I Trust You?": Explaining the Predictions of Any Classifier (LIME)
• 2016-ICMLw-The Mythos of Model Interpretability
• 2017-NIPSw-The (Un)reliability of saliency methods
• 2017-DSP-Methods for interpreting and understanding deep neural networks
• 2018-ICML-Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)
• 2018-NIPSs-Sanity Checks for Saliency Maps
• 2018-NIPSs-Human-in-the-Loop Interpretability Prior
• 2018-NIPS-To Trust Or Not To Trust A Classifier [Code]
• 2019-AISTATS-Interpreting Black Box Predictions using Fisher Kernels
• 2019.05-Luck Matters: Understanding Training Dynamics of Deep ReLU Networks
• 2019.05-Adversarial Examples Are Not Bugs, They Are Features
• 2019.06-The Generalization-Stability Tradeoff in Neural Network Pruning
• 2019.06-One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers
• 2019-Book-Interpretable Machine Learning

Papers-Knowledge Distillation

• 1996-Born again trees (proposed compressing neural networks and multipletree predictors by approximating them with a single tree)
• 2006-SIGKDD-Model compression
• 2010-ML-A theory of learning from different domains
• 2014-NIPS-Do deep nets really need to be deep?
• 2014-NIPSw-Distilling the Knowledge in a Neural Network (coined the name "knowledge distillation" and "dark knowledge") [Code]
• 2015-NIPS-Bayesian dark knowledge
• 2016-ICLR-Net2net: Accelerating learning via knowledge transfer (Tianqi Chen and Goodfellow)
• 2016-ECCV-Accelerating convolutional neural networks with dominant convolutional kernel and knowledge pre-regression
• 2017-ICLR-Paying more attention to attention: Improving the performance of convolutional neural networksvia attention transfer
• 2017-ICLR-Do deep convolutional nets really need to be deep and convolutional?
• 2017-CVPR-A gift from knowledge distillation: Fast optimization, network minimization and transfer learning
• 2017-BMVC-Adapting models to signal degradation using distillation
• 2017-NIPS-Sobolev training for neural networks
• 2017-NIPS-Learning efficient object detection models with knowledge distillation
• 2017-NIPSw-Data-Free Knowledge Distillation for Deep Neural Networks [Code]
• 2017.07-Like What You Like: Knowledge Distill via Neuron Selectivity Transfer
• 2017.10-Knowledge Projection for Deep Neural Networks
• 2017.11-Distilling a Neural Network Into a Soft Decision Tree
• 2017.12-Data Distillation: Towards Omni-Supervised Learning (Kaiming He)
• 2018.03-Interpreting Deep Classifier by Visual Distillation of Dark Knowledge
• 2018.11-Dataset Distillation [Code]
• 2018.12-Learning Student Networks via Feature Embedding
• 2018.12-Few Sample Knowledge Distillation for Efficient Network Compression
• 2018-AAAI-DarkRank: Accelerating Deep Metric Learning via Cross Sample Similarities Transfer
• 2018-AAAI-Dynamic deep neural networks: Optimizing accuracy-efficiency trade-offs by selective execution
• 2018-AAAI-Rocket Launching: A Universal and Efficient Framework for Training Well-performing Light Net
• 2018-AAAI-Adversarial Learning of Portable Student Networks
• 2018-AAAI-Knowledge Distillation in Generations: More Tolerant Teachers Educate Better Students
• 2018-ICLR-Large scale distributed neural network training through online distillation
• 2018-CVPR-Deep mutual learning
• 2018-ICML-Born-Again Neural Networks
• 2018-IJCAI-Better and Faster: Knowledge Transfer from Multiple Self-supervised Learning Tasks via Graph Distillation for Video Classification
• 2018-ECCV-2018-ECCV-Learning deep representations with probabilistic knowledge transfer [Code]
• 2018-ECCV-Graph adaptive knowledge transfer for unsupervised domain adaptation
• 2018-SIGKDD-Towards Evolutionary Compression
• 2018-NIPS-KDGAN: knowledge distillation with generative adversarial networks
• 2018-NIPS-Knowledge Distillation by On-the-Fly Native Ensemble
• 2018-NIPSw-Variational Mutual Information Distillation for Transfer Learning workshop: continual learning
• 2018-NIPSw-Transparent Model Distillation
• 2019-AAAI-Knowledge Distillation with Adversarial Samples Supporting Decision Boundary
• 2019-AAAI-Knowledge Transfer via Distillation of Activation Boundaries Formed by Hidden Neurons [Code]
• 2019-AAAI-Learning to Steer by Mimicking Features from Heterogeneous Auxiliary Networks [Code]
• 2019-CVPR-Knowledge Representing: Efficient, Sparse Representation of Prior Knowledge for Knowledge Distillation
• 2019-CVPR-Knowledge Distillation via Instance Relationship Graph
• 2019-CVPR-Variational Information Distillation for Knowledge Transfer
• 2019-CVPR-Learning Metrics from Teachers Compact Networks for Image Embedding [Code]
• 2019-ICCV-Similarity-Preserving Knowledge Distillation
• 2019-ICCV-Correlation Congruence for Knowledge Distillation
• 2019-ICCV-Data-Free Learning of Student Networks
• 2019-ICCV-Learning Lightweight Lane Detection CNNs by Self Attention Distillation [Code]
• 2019-NIPS-Zero-shot Knowledge Transfer via Adversarial Belief Matching [Code]
• 2019.05-DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

People (in alphabeta order)

• Been Kim @ Google Brain (Interpretability)
• Elliot Crowley @ Edinburgh
• Gao Huang @ Tsinghua
• Hanting Chen @ PKU
• Mingjie Sun @ BUAA
• Mohsen Imani @ UCSD
• Naiyan Wang @ TuSimple
• Ji Lin @ MIT
• Jianguo Li @ Intel
• Miguel Carreira-Perpinan @ UC Merced
• Pavlo Molchanov @ NVIDIA
• Song Han @ MIT
• Wei Wen @ Duke
• Xiaohan Ding @ Tsinghua
• Xin Dong @ Harvard
• Yang He @ University of Technology Sydney
• Yanzhi Wang @ NEU US
• Yihui He @ CMU
• Yunhe Wang @ Huawei
• Zhuang Liu @ UC Berkeley

People in NAS (in alphabeta order)

• Marius Lindauer @ University of Freiburg
• Quoc V. Le @ Google Brain

Venues

• OpenReview
• ICLR 2015
• ICLR
• CVPR & ICCV
• ECCV
• NIPS
• 2017-ICML Tutorial: interpretable machine learning
• 2018-AAAI
• 2018-ICLR
• 2018-ICML
• 2018-ICML Workshop: Efficient Credit Assignment in Deep Learning and Reinforcement Learning
• 2018-IJCAI
• 2018-BMVC
• COLT: 2019
• SysML: 2018, 2019, 2020
• CDNNRIA Workshop (Compact Deep Neural Network Representation with Industrial Applications): 1st-2018-NIPSw, 2nd-2019-ICMLw
• LLD Workshop (Learning with Limited Data): 1st-2017-NIPSw, 2nd-2019-ICLRw
• WHI (Worshop on Human Interpretability in Machine Learning): 1st-2016-ICMLw, 2nd-2017-ICMLw, 3rd-2018-ICMLw
• NIPS-18 Workshop on Systems for ML and Open Source Software
• MLPCD Workshop (Machine Learning on the Phone and other Consumer Devices): 2nd-2018-NIPSw

Lightweight DNN Engines/APIs

• NNPACK
• DMLC: Tensor Virtual Machine (TVM): Open Deep Learning Compiler Stack
• Tencent: NCNN
• Xiaomi: MACE, Mobile AI Benchmark
• Alibaba: MNN blog (in Chinese)
• Baidu: Paddle-Slim, Paddle-Mobile, Anakin
• Microsoft: ELL, AutoML tool NNI
• Facebook: Caffe2/PyTorch
• Apple: CoreML (iOS 11+)
• Google: ML-Kit, NNAPI (Android 8.1+), TF-Lite
• Qualcomm: Snapdragon Neural Processing Engine (SNPE), Adreno GPU SDK
• Huawei: HiAI
• ARM: Tengine
• Related: DAWNBench: An End-to-End Deep Learning Benchmark and Competition

Related Repos and Websites

• caffe-int8-convert-tools
• Neural-Networks-on-Silicon
• Embedded-Neural-Network
• Papers with code
• Awesome-NAS
• Awesome-Pruning
• AutoML NAS Literature

News

• VALSE 2018年度进展报告 | 深度神经网络加速与压缩 (in Chinese)
• 机器之心-腾讯AI Lab PocketFlow (in Chinese)
• 阿里开源首个移动AI项目，淘宝同款推理引擎 (in Chinese)
• 精度无损，体积压缩70%以上，百度PaddleSlim为你的模型瘦身 (in Chinese). Its github project.