CyCADA: Cycle-Consistent Adversarial Domain Adaptation

Abstract

Domain adaptation is critical for success in new, unseen environments. Adversarial adaptation models applied in feature spaces discover domain invariant representations, but are difficult to visualize and sometimes fail to capture pixel-level and low-level domain shifts. Recent work has shown that generative adversarial networks combined with cycle-consistency constraints are surprisingly effective at mapping images between domains, even without the use of aligned image pairs. We propose a novel discriminatively-trained Cycle-Consistent Adversarial Domain Adaptation model. CyCADA adapts representations at both the pixel-level and feature-level, enforces cycle-consistency while leveraging a task loss, and does not require aligned pairs. Our model can be applied in a variety of visual recognition and prediction settings. We show new state-of-the-art results across multiple adaptation tasks, including digit classification and semantic segmentation of road scenes demonstrating transfer from synthetic to real world domains.

References in corpus (14)

• Distilling the Knowledge in a Neural Network
• Conditional Generative Adversarial Nets
• Unsupervised Domain Adaptation by Backpropagation
• Deep Domain Confusion: Maximizing for Domain Invariance
• Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks
• Energy-based Generative Adversarial Network
• FCNs in the Wild: Pixel-level Adversarial and Constraint-based Adaptation
• Adversarial Discriminative Domain Adaptation
• Unsupervised Cross-Domain Image Generation
• Deep CORAL: Correlation Alignment for Deep Domain Adaptation
• Dilated Residual Networks
• Playing for Data: Ground Truth from Computer Games
• Learning to Generate Images of Outdoor Scenes from Attributes and Semantic Layouts
• Scribbler: Controlling Deep Image Synthesis with Sketch and Color

Cited by in corpus (6)

• Significance-aware Information Bottleneck for Domain Adaptive Semantic Segmentation
• All about Structure: Adapting Structural Information across Domains for Boosting Semantic Segmentation
• ACE: Adapting to Changing Environments for Semantic Segmentation
• TAFE-Net: Task-Aware Feature Embeddings for Low Shot Learning
• Domain Alignment with Triplets
• Learning to see across Domains and Modalities