Co-regularized Alignment for Unsupervised Domain Adaptation

by   Abhishek Kumar, et al.

Deep neural networks, trained with large amount of labeled data, can fail to generalize well when tested with examples from a target domain whose distribution differs from the training data distribution, referred as the source domain. It can be expensive or even infeasible to obtain required amount of labeled data in all possible domains. Unsupervised domain adaptation sets out to address this problem, aiming to learn a good predictive model for the target domain using labeled examples from the source domain but only unlabeled examples from the target domain. Domain alignment approaches this problem by matching the source and target feature distributions, and has been used as a key component in many state-of-the-art domain adaptation methods. However, matching the marginal feature distributions does not guarantee that the corresponding class conditional distributions will be aligned across the two domains. We propose co-regularized domain alignment for unsupervised domain adaptation, which constructs multiple diverse feature spaces and aligns source and target distributions in each of them individually, while encouraging that alignments agree with each other with regard to the class predictions on the unlabeled target examples. The proposed method is generic and can be used to improve any domain adaptation method which uses domain alignment. We instantiate it in the context of a recent state-of-the-art method and observe that it provides significant performance improvements on several domain adaptation benchmarks.


Cluster Alignment with a Teacher for Unsupervised Domain Adaptation

Deep learning methods have shown promise in unsupervised domain adaptati...

Domain Adaptation and Image Classification via Deep Conditional Adaptation Network

Unsupervised domain adaptation aims to generalize the supervised model t...

Multi-Source domain adaptation via supervised contrastive learning and confident consistency regularization

Multi-Source Unsupervised Domain Adaptation (multi-source UDA) aims to l...

Using Latent Codes for Class Imbalance Problem in Unsupervised Domain Adaptation

We address the problem of severe class imbalance in unsupervised domain ...

Just DIAL: DomaIn Alignment Layers for Unsupervised Domain Adaptation

The empirical fact that classifiers, trained on given data collections, ...

SALT: Subspace Alignment as an Auxiliary Learning Task for Domain Adaptation

Unsupervised domain adaptation aims to transfer and adapt knowledge lear...

Covariance-aware Feature Alignment with Pre-computed Source Statistics for Test-time Adaptation

The accuracy of deep neural networks is degraded when the distribution o...

Please sign up or login with your details

Forgot password? Click here to reset