# Co-regularized Alignment for Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/1811.05443 · HTML: https://mlsanity.com/paper/1811.05443/

## Key facts

- Cited by 39 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Nov 2018.

## Built on by (most cited first, 30 of 39)

- [Prototypical Pseudo Label Denoising and Target Structure Learning for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2101.10979/) (2021)
- [Deep Subdomain Adaptation Network for Image Classification](https://mlsanity.com/paper/2106.09388/) (2021)
- [Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives](https://mlsanity.com/paper/2208.07422/) (2022)
- [Pin the Memory: Learning to Generalize Semantic Segmentation](https://mlsanity.com/paper/2204.03609/) (2022)
- [Your Classifier can Secretly Suffice Multi-Source Domain Adaptation](https://mlsanity.com/paper/2103.11169/) (2021)
- [Task-specific Inconsistency Alignment for Domain Adaptive Object Detection](https://mlsanity.com/paper/2203.15345/) (2022)
- [Generation, augmentation, and alignment: A pseudo-source domain based method for source-free domain adaptation](https://mlsanity.com/paper/2109.04015/) (2021)
- [Instance Level Affinity-Based Transfer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2104.01286/) (2021)
- [Multi-Representation Adaptation Network for Cross-domain Image Classification](https://mlsanity.com/paper/2201.01002/) (2022)
- [GeoNet: Benchmarking Unsupervised Adaptation across Geographies](https://mlsanity.com/paper/2303.15443/) (2023)
- [MemSAC: Memory Augmented Sample Consistency for Large Scale Unsupervised Domain Adaptation](https://mlsanity.com/paper/2207.12389/) (2022)
- [Limitations of Post-Hoc Feature Alignment for Robustness](https://mlsanity.com/paper/2103.05898/) (2021)
- [UDApter -- Efficient Domain Adaptation Using Adapters](https://mlsanity.com/paper/2302.03194/) (2023)
- [Undoing the Damage of Label Shift for Cross-domain Semantic Segmentation](https://mlsanity.com/paper/2204.05546/) (2022)
- [Domain Adaptive Video Segmentation via Temporal Pseudo Supervision](https://mlsanity.com/paper/2207.02372/) (2022)
- [ProCST: Boosting Semantic Segmentation Using Progressive Cyclic Style-Transfer](https://mlsanity.com/paper/2204.11891/) (2022)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [Large Self-Supervised Models Bridge the Gap in Domain Adaptive Object Detection](https://mlsanity.com/paper/2503.23220/) (2025)
- [Mixup Regularized Adversarial Networks for Multi-Domain Text Classification](https://mlsanity.com/paper/2102.00467/) (2021)
- [Normalization Perturbation: A Simple Domain Generalization Method for Real-World Domain Shifts](https://mlsanity.com/paper/2211.04393/) (2022)
- [UDA-Bench: Revisiting Common Assumptions in Unsupervised Domain Adaptation Using a Standardized Framework](https://mlsanity.com/paper/2409.15264/) (2024)
- [Adversarial Support Alignment](https://mlsanity.com/paper/2203.08908/) (2022)
- [Domain Adaptation with Adversarial Training on Penultimate Activations](https://mlsanity.com/paper/2208.12853/) (2022)
- [DAPPER: Label-Free Performance Estimation after Personalization for Heterogeneous Mobile Sensing](https://mlsanity.com/paper/2111.11053/) (2021)
- [Tell, Don't Show!: Language Guidance Eases Transfer Across Domains in Images and Videos](https://mlsanity.com/paper/2403.05535/) (2024)
- [Knowledge-inspired Subdomain Adaptation for Cross-Domain Knowledge Transfer](https://mlsanity.com/paper/2308.09724/) (2023)
- [StitchNet: Composing Neural Networks from Pre-Trained Fragments](https://mlsanity.com/paper/2301.01947/) (2023)
- [Revisiting Fine-Tuning Strategies for Self-supervised Medical Imaging Analysis](https://mlsanity.com/paper/2307.10915/) (2023)
- [DART: A Principled Approach to Adversarially Robust Unsupervised Domain Adaptation](https://mlsanity.com/paper/2402.11120/) (2024)
- [ADeLA: Automatic Dense Labeling with Attention for Viewpoint Adaptation in Semantic Segmentation](https://mlsanity.com/paper/2107.14285/) (2021)

## Abstract (arXiv)

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.