Domain-Invariant Adversarial Learning for Unsupervised Domain Adaption

Abstract

Unsupervised domain adaption aims to learn a powerful classifier for the target domain given a labeled source data set and an unlabeled target data set. To alleviate the effect of `domain shift', the major challenge in domain adaptation, studies have attempted to align the distributions of the two domains. Recent research has suggested that generative adversarial network (GAN) has the capability of implicitly capturing data distribution. In this paper, we thus propose a simple but effective model for unsupervised domain adaption leveraging adversarial learning. The same encoder is shared between the source and target domains which is expected to extract domain-invariant representations with the help of an adversarial discriminator. With the labeled source data, we introduce the center loss to increase the discriminative power of feature learned. We further align the conditional distribution of the two domains to enforce the discrimination of the features in the target domain. Unlike previous studies where the source features are extracted with a fixed pre-trained encoder, our method jointly learns feature representations of two domains. Moreover, by sharing the encoder, the model does not need to know the source of images during testing and hence is more widely applicable. We evaluate the proposed method on several unsupervised domain adaption benchmarks and achieve superior or comparable performance to state-of-the-art results.

References in corpus (11)

• Very Deep Convolutional Networks for Large-Scale Image Recognition
• Learning Transferable Features with Deep Adaptation Networks
• Deep Domain Confusion: Maximizing for Domain Invariance
• Unsupervised Domain Adaptation with Residual Transfer Networks
• Domain Separation Networks
• Beyond Sharing Weights for Deep Domain Adaptation
• Adversarial Discriminative Domain Adaptation
• Unsupervised Cross-Domain Image Generation
• Deep CORAL: Correlation Alignment for Deep Domain Adaptation
• Central Moment Discrepancy (CMD) for Domain-Invariant Representation Learning
• Deep Transfer Learning with Joint Adaptation Networks

Cited by in corpus (5)

• Learning Disentangled Semantic Representation for Domain Adaptation
• When is invariance useful in an Out-of-Distribution Generalization problem ?
• Joint Semantic Domain Alignment and Target Classifier Learning for Unsupervised Domain Adaptation
• Discriminative Consistent Domain Generation for Semi-supervised Learning
• Learning Task-oriented Disentangled Representations for Unsupervised Domain Adaptation