# Contrastive Domain Adaptation

arXiv: https://arxiv.org/abs/2103.15566 · HTML: https://mlsanity.com/paper/2103.15566/

## Key facts

- Cited by 36 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 46 references that are in the map.
- First posted on arXiv 26 Mar 2021.

## Builds on (most cited first, 28 of 28)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Representation Learning with Contrastive Predictive Coding](https://mlsanity.com/paper/1807.03748/) (2018)
- [Momentum Contrast for Unsupervised Visual Representation Learning](https://mlsanity.com/paper/1911.05722/) (2019)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination](https://mlsanity.com/paper/1805.01978/) (2018)
- [Big Self-Supervised Models are Strong Semi-Supervised Learners](https://mlsanity.com/paper/2006.10029/) (2020)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Contrastive Multiview Coding](https://mlsanity.com/paper/1906.05849/) (2019)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Contrastive Learning with Hard Negative Samples](https://mlsanity.com/paper/2010.04592/) (2020)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [Deep Domain Confusion: Maximizing for Domain Invariance](https://mlsanity.com/paper/1412.3474/) (2014)
- [Large Batch Training of Convolutional Networks](https://mlsanity.com/paper/1708.03888/) (2017)
- [Contrastive Adaptation Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1901.00976/) (2019)
- [Hard Negative Mixing for Contrastive Learning](https://mlsanity.com/paper/2010.01028/) (2020)
- [Debiased Contrastive Learning](https://mlsanity.com/paper/2007.00224/) (2020)
- [Sliced Wasserstein Discrepancy for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1903.04064/) (2019)
- [Coupled Generative Adversarial Networks](https://mlsanity.com/paper/1606.07536/) (2016)
- [Boosting Contrastive Self-Supervised Learning with False Negative Cancellation](https://mlsanity.com/paper/2011.11765/) (2020)
- [Deep Learning for Audio Signal Processing](https://mlsanity.com/paper/1905.00078/) (2019)
- [Recent Advances in Deep Learning for Object Detection](https://mlsanity.com/paper/1908.03673/) (2019)
- [Joint Domain Alignment and Discriminative Feature Learning for Unsupervised Deep Domain Adaptation](https://mlsanity.com/paper/1808.09347/) (2018)
- [Deep Object-Centric Policies for Autonomous Driving](https://mlsanity.com/paper/1811.05432/) (2018)
- [Joint Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2006.10297/) (2020)
- [Cross-domain Self-supervised Learning for Domain Adaptation with Few Source Labels](https://mlsanity.com/paper/2003.08264/) (2020)
- [An autoencoder wavelet based deep neural network with attention mechanism for multistep prediction of plant growth](https://mlsanity.com/paper/2012.04041/) (2020)

## Built on by (most cited first, 30 of 36)

- [Cross-domain Contrastive Learning for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.05528/) (2021)
- [Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations](https://mlsanity.com/paper/2204.02683/) (2022)
- [CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning](https://mlsanity.com/paper/2109.14778/) (2021)
- [FairDisCo: Fairer AI in Dermatology via Disentanglement Contrastive Learning](https://mlsanity.com/paper/2208.10013/) (2022)
- [Contrastive Domain Adaptation for Time-Series via Temporal Mixup](https://mlsanity.com/paper/2212.01555/) (2022)
- [Hard Negative Sampling Strategies for Contrastive Representation Learning](https://mlsanity.com/paper/2206.01197/) (2022)
- [Bayesian Learning-driven Prototypical Contrastive Loss for Class-Incremental Learning](https://mlsanity.com/paper/2405.11067/) (2024)
- [REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter](https://mlsanity.com/paper/2104.14118/) (2021)
- [Semi-supervised Domain Adaptive Structure Learning](https://mlsanity.com/paper/2112.06161/) (2021)
- [Domain Adaptation via Maximizing Surrogate Mutual Information](https://mlsanity.com/paper/2110.12184/) (2021)
- [DeepAstroUDA: Semi-Supervised Universal Domain Adaptation for Cross-Survey Galaxy Morphology Classification and Anomaly Detection](https://mlsanity.com/paper/2302.02005/) (2023)
- [Model Pruning Enables Localized and Efficient Federated Learning for Yield Forecasting and Data Sharing](https://mlsanity.com/paper/2304.09876/) (2023)
- [Pseudo Label Refinery for Unsupervised Domain Adaptation on Cross-dataset 3D Object Detection](https://mlsanity.com/paper/2404.19384/) (2024)
- [ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection](https://mlsanity.com/paper/2407.01312/) (2024)
- [Knowledge Sharing via Domain Adaptation in Customs Fraud Detection](https://mlsanity.com/paper/2201.06759/) (2022)
- [Test-Time Training for Semantic Segmentation with Output Contrastive Loss](https://mlsanity.com/paper/2311.07877/) (2023)
- [Unsupervised Contrastive Learning for Robust RF Device Fingerprinting Under Time-Domain Shift](https://mlsanity.com/paper/2403.04036/) (2024)
- [A Self-explaining Neural Architecture for Generalizable Concept Learning](https://mlsanity.com/paper/2405.00349/) (2024)
- [Learning from Synthetic InSAR with Vision Transformers: The case of volcanic unrest detection](https://mlsanity.com/paper/2201.03016/) (2022)
- [LLEDA -- Lifelong Self-Supervised Domain Adaptation](https://mlsanity.com/paper/2211.09027/) (2022)
- [Contrast and Clustering: Learning Neighborhood Pair Representation for Source-free Domain Adaptation](https://mlsanity.com/paper/2301.13428/) (2023)
- [COSTAR: Improved Temporal Counterfactual Estimation with Self-Supervised Learning](https://mlsanity.com/paper/2311.00886/) (2023)
- [ST-DAI: Single-shot 2.5D Spatial Transcriptomics with Intra-Sample Domain Adaptive Imputation for Cost-efficient 3D Reconstruction](https://mlsanity.com/paper/2507.21516/) (2025)
- [EDLaaS: Fully Homomorphic Encryption Over Neural Network Graphs for Vision and Private Strawberry Yield Forecasting](https://mlsanity.com/paper/2110.13638/) (2021)
- [Stable and Causal Inference for Discriminative Self-supervised Deep Visual Representations](https://mlsanity.com/paper/2308.08321/) (2023)
- [Speech Separation with Pretrained Frontend to Minimize Domain Mismatch](https://mlsanity.com/paper/2411.03085/) (2024)
- [Bridging Contrastive Learning and Domain Adaptation: Theoretical Perspective and Practical Application](https://mlsanity.com/paper/2502.00052/) (2025)
- [Cross-domain Transfer of defect features in technical domains based on partial target data](https://mlsanity.com/paper/2211.13662/) (2022)
- [DomainInv: Domain Invariant Fine Tuning and Adversarial Label Correction For QA Domain Adaptation](https://mlsanity.com/paper/2305.05589/) (2023)
- [Gradually Vanishing Gap in Prototypical Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2405.17774/) (2024)

## Abstract (arXiv)

Recently, contrastive self-supervised learning has become a key component for learning visual representations across many computer vision tasks and benchmarks. However, contrastive learning in the context of domain adaptation remains largely underexplored. In this paper, we propose to extend contrastive learning to a new domain adaptation setting, a particular situation occurring where the similarity is learned and deployed on samples following different probability distributions without access to labels. Contrastive learning learns by comparing and contrasting positive and negative pairs of samples in an unsupervised setting without access to source and target labels. We have developed a variation of a recently proposed contrastive learning framework that helps tackle the domain adaptation problem, further identifying and removing possible negatives similar to the anchor to mitigate the effects of false negatives. Extensive experiments demonstrate that the proposed method adapts well, and improves the performance on the downstream domain adaptation task.