cycada.md

CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Judy Hoffman, Eric Tzeng, Taesung Park, Jun-Yan Zhu, Phillip Isola, Kate Saenko, Alexei A. Efros, Trevor Darrell, ICML-2018

Summary

This paper proposes a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. Leveraging the cycle-consistency, the model does not require aligned pairs and the author claims state-of-the-art results across multiple domain adaptation tasks.

Main Contributions

• The main contribution of the paper is its novel techique for domain adaptation using cycle-consistency losses, taking inspiration from the CycleGAN paper. But while CycleGAN produced task-agnostic domain transfer, this model has been trained for various particular tasks.

• Provided is source data X_s, source labels Y_s, target data X_t, but no target labels. The goal is to learn a model f to correctly predict label for target data X_t.

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