# Casting a BAIT for Offline and Online Source-free Domain Adaptation

arXiv: https://arxiv.org/abs/2010.12427 · HTML: https://mlsanity.com/paper/2010.12427/

## Key facts

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

## Built on by (most cited first, 23 of 23)

- [A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts](https://mlsanity.com/paper/2303.15361/) (2023)
- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [Source-Free Unsupervised Domain Adaptation: A Survey](https://mlsanity.com/paper/2301.00265/) (2022)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning](https://mlsanity.com/paper/2211.06612/) (2022)
- [Improving the Generalization of Segmentation Foundation Model under Distribution Shift via Weakly Supervised Adaptation](https://mlsanity.com/paper/2312.03502/) (2023)
- [TeSLA: Test-Time Self-Learning With Automatic Adversarial Augmentation](https://mlsanity.com/paper/2303.09870/) (2023)
- [Cross-Modal Knowledge Transfer Without Task-Relevant Source Data](https://mlsanity.com/paper/2209.04027/) (2022)
- [Consistency Regularization for Generalizable Source-free Domain Adaptation](https://mlsanity.com/paper/2308.01587/) (2023)
- [Uncertainty-Induced Transferability Representation for Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2208.13986/) (2022)
- [In Search for a Generalizable Method for Source Free Domain Adaptation](https://mlsanity.com/paper/2302.06658/) (2023)
- [Direct Distillation between Different Domains](https://mlsanity.com/paper/2401.06826/) (2024)
- [Jacobian Norm for Unsupervised Source-Free Domain Adaptation](https://mlsanity.com/paper/2204.03467/) (2022)
- [Temporal Source Recovery for Time-Series Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2409.19635/) (2024)
- [Curriculum Guided Domain Adaptation in the Dark](https://mlsanity.com/paper/2308.00956/) (2023)
- [Diffusion-Driven Progressive Target Manipulation for Source-Free Domain Adaptation](https://mlsanity.com/paper/2510.25279/) (2025)
- [Confidence Attention and Generalization Enhanced Distillation for Continuous Video Domain Adaptation](https://mlsanity.com/paper/2303.10452/) (2023)
- [FREEDOM: Target Label & Source Data & Domain Information-Free Multi-Source Domain Adaptation for Unsupervised Personalization](https://mlsanity.com/paper/2307.02493/) (2023)
- [Improving Online Source-free Domain Adaptation for Object Detection by Unsupervised Data Acquisition](https://mlsanity.com/paper/2310.19258/) (2023)
- [Source-Free Unsupervised Domain Adaptation with Hypothesis Consolidation of Prediction Rationale](https://mlsanity.com/paper/2402.01157/) (2024)
- [Knowledge-Data Fusion Based Source-Free Semi-Supervised Domain Adaptation for Seizure Subtype Classification](https://mlsanity.com/paper/2411.19502/) (2024)
- [Shuffle PatchMix Augmentation with Confidence-Margin Weighted Pseudo-Labels for Enhanced Source-Free Domain Adaptation](https://mlsanity.com/paper/2505.24216/) (2025)
- [Topology-Aware Neighborhood Learning for Source-Free Cross-Scene Hyperspectral Image Classification](https://mlsanity.com/paper/2608.05964/) (2026)

## Abstract (arXiv)

We address the source-free domain adaptation (SFDA) problem, where only the source model is available during adaptation to the target domain. We consider two settings: the offline setting where all target data can be visited multiple times (epochs) to arrive at a prediction for each target sample, and the online setting where the target data needs to be directly classified upon arrival. Inspired by diverse classifier based domain adaptation methods, in this paper we introduce a second classifier, but with another classifier head fixed. When adapting to the target domain, the additional classifier initialized from source classifier is expected to find misclassified features. Next, when updating the feature extractor, those features will be pushed towards the right side of the source decision boundary, thus achieving source-free domain adaptation. Experimental results show that the proposed method achieves competitive results for offline SFDA on several benchmark datasets compared with existing DA and SFDA methods, and our method surpasses by a large margin other SFDA methods under online source-free domain adaptation setting.