# Discriminative Feature Alignment: Improving Transferability of Unsupervised Domain Adaptation by Gaussian-guided Latent Alignment

arXiv: https://arxiv.org/abs/2006.12770 · HTML: https://mlsanity.com/paper/2006.12770/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Jun 2020.

## Built on by (most cited first, 24 of 24)

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- [Positive-Unlabeled Domain Adaptation](https://mlsanity.com/paper/2202.05695/) (2022)
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- [SMILE: Sequence-to-Sequence Domain Adaption with Minimizing Latent Entropy for Text Image Recognition](https://mlsanity.com/paper/2202.11949/) (2022)
- [Ensemble diverse hypotheses and knowledge distillation for unsupervised cross-subject adaptation](https://mlsanity.com/paper/2204.07308/) (2022)
- [Unsupervised Domain Adaptation via Style-Aware Self-intermediate Domain](https://mlsanity.com/paper/2209.01870/) (2022)
- [Distribution Aligned Feature Clustering for Zero-Shot Sketch-Based Image Retrieval](https://mlsanity.com/paper/2301.06685/) (2023)
- [DAFD: Domain Adaptation via Feature Disentanglement for Image Classification](https://mlsanity.com/paper/2301.13337/) (2023)
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- [Semi-Supervised Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/2109.13016/) (2021)
- [Bures Joint Distribution Alignment with Dynamic Margin for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2203.06836/) (2022)
- [Contrastive Bi-Projector for Unsupervised Domain Adaption](https://mlsanity.com/paper/2308.07017/) (2023)
- [A Weight-aware-based Multi-source Unsupervised Domain Adaptation Method for Human Motion Intention Recognition](https://mlsanity.com/paper/2404.15366/) (2024)
- [Contrastive Adversarial Training for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2407.12782/) (2024)
- [Day-Night Adaptation: An Innovative Source-free Adaptation Framework for Medical Image Segmentation](https://mlsanity.com/paper/2410.13472/) (2024)
- [PARDON: Privacy-Aware and Robust Federated Domain Generalization](https://mlsanity.com/paper/2410.22622/) (2024)
- [Toward Real-World IoT Security: Concept Drift-Resilient IoT Botnet Detection via Latent Space Representation Learning and Alignment](https://mlsanity.com/paper/2512.22488/) (2025)
- [Multi-Dataset Cross-Domain Knowledge Distillation for Unified Medical Image Segmentation, Classification, and Detection](https://mlsanity.com/paper/2605.01563/) (2026)

## Abstract (arXiv)

In this study, we focus on the unsupervised domain adaptation problem where an approximate inference model is to be learned from a labeled data domain and expected to generalize well to an unlabeled data domain. The success of unsupervised domain adaptation largely relies on the cross-domain feature alignment. Previous work has attempted to directly align latent features by the classifier-induced discrepancies. Nevertheless, a common feature space cannot always be learned via this direct feature alignment especially when a large domain gap exists. To solve this problem, we introduce a Gaussian-guided latent alignment approach to align the latent feature distributions of the two domains under the guidance of the prior distribution. In such an indirect way, the distributions over the samples from the two domains will be constructed on a common feature space, i.e., the space of the prior, which promotes better feature alignment. To effectively align the target latent distribution with this prior distribution, we also propose a novel unpaired L1-distance by taking advantage of the formulation of the encoder-decoder. The extensive evaluations on nine benchmark datasets validate the superior knowledge transferability through outperforming state-of-the-art methods and the versatility of the proposed method by improving the existing work significantly.