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A Strategy for Label Alignment in Deep Neural Networks

Xuanrui Zeng

TL;DR

The paper tackles unsupervised domain adaptation by leveraging a label alignment property that ties label variation to the top principal components of the representation. Building on prior linear regression work, it derives a deep label alignment formulation that uses a learnable rank k with soft gating to form reduced feature representations and combines a standard source supervision loss with a target label alignment loss in an end-to-end objective. The approach extends to deep networks by mapping inputs through a feature extractor f and a predictor g, producing Phi' and Phi tilde' via an SVD inspired decomposition, and training with loss terms and hyperparameters lambda and gamma to balance alignment. Empirical results on MNIST to USPS show that deep label alignment yields stable training and competitive target accuracies relative to adversarial methods such as ADDA and DANN, while offering a non adversarial optimization signal and a public codebase for replication. The work highlights potential as a regularizer through the partial label alignment observation and opens avenues for rigorous theory and broader task evaluation.

Abstract

One recent research demonstrated successful application of the label alignment property for unsupervised domain adaptation in a linear regression settings. Instead of regularizing representation learning to be domain invariant, the research proposed to regularize the linear regression model to align with the top singular vectors of the data matrix from the target domain. In this work we expand upon this idea and generalize it to the case of deep learning, where we derive an alternative formulation of the original adaptation algorithm exploiting label alignment suitable for deep neural network. We also perform experiments to demonstrate that our approach achieves comparable performance to mainstream unsupervised domain adaptation methods while having stabler convergence. All experiments and implementations in our work can be found at the following codebase: https://github.com/xuanrui-work/DeepLabelAlignment.

A Strategy for Label Alignment in Deep Neural Networks

TL;DR

The paper tackles unsupervised domain adaptation by leveraging a label alignment property that ties label variation to the top principal components of the representation. Building on prior linear regression work, it derives a deep label alignment formulation that uses a learnable rank k with soft gating to form reduced feature representations and combines a standard source supervision loss with a target label alignment loss in an end-to-end objective. The approach extends to deep networks by mapping inputs through a feature extractor f and a predictor g, producing Phi' and Phi tilde' via an SVD inspired decomposition, and training with loss terms and hyperparameters lambda and gamma to balance alignment. Empirical results on MNIST to USPS show that deep label alignment yields stable training and competitive target accuracies relative to adversarial methods such as ADDA and DANN, while offering a non adversarial optimization signal and a public codebase for replication. The work highlights potential as a regularizer through the partial label alignment observation and opens avenues for rigorous theory and broader task evaluation.

Abstract

One recent research demonstrated successful application of the label alignment property for unsupervised domain adaptation in a linear regression settings. Instead of regularizing representation learning to be domain invariant, the research proposed to regularize the linear regression model to align with the top singular vectors of the data matrix from the target domain. In this work we expand upon this idea and generalize it to the case of deep learning, where we derive an alternative formulation of the original adaptation algorithm exploiting label alignment suitable for deep neural network. We also perform experiments to demonstrate that our approach achieves comparable performance to mainstream unsupervised domain adaptation methods while having stabler convergence. All experiments and implementations in our work can be found at the following codebase: https://github.com/xuanrui-work/DeepLabelAlignment.
Paper Structure (7 sections, 9 equations, 2 figures, 1 table, 1 algorithm)

This paper contains 7 sections, 9 equations, 2 figures, 1 table, 1 algorithm.

Table of Contents

  1. Introduction
  2. Techniques
  3. Previous Work from Imani et al. (2022)
  4. Another Perspective
  5. Onto Deep Neural Networks
  6. Evaluation
  7. Conclusion

Figures (2)

  • Figure 1: Architecture of the image classification network for our experiment.
  • Figure 2: Training curves of: (a) our approach, (b) ADDA, and (c) DANN. Comparing to ADDA and DANN, our approach shows a stabler convergence for the classification loss in the target domain.