# Bi-Classifier Determinacy Maximization for Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/2012.06995 · HTML: https://mlsanity.com/paper/2012.06995/

## Key facts

- Cited by 35 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Dec 2020.

## Built on by (most cited first, 30 of 35)

- [Causality Inspired Representation Learning for Domain Generalization](https://mlsanity.com/paper/2203.14237/) (2022)
- [A Comprehensive Survey on Source-free Domain Adaptation](https://mlsanity.com/paper/2302.11803/) (2023)
- [Reusing the Task-specific Classifier as a Discriminator: Discriminator-free Adversarial Domain Adaptation](https://mlsanity.com/paper/2204.03838/) (2022)
- [Active Learning for Domain Adaptation: An Energy-Based Approach](https://mlsanity.com/paper/2112.01406/) (2021)
- [Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning](https://mlsanity.com/paper/2211.06612/) (2022)
- [Semantic Concentration for Domain Adaptation](https://mlsanity.com/paper/2108.05720/) (2021)
- [Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2209.07695/) (2022)
- [Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving](https://mlsanity.com/paper/2304.11928/) (2023)
- [SPA: A Graph Spectral Alignment Perspective for Domain Adaptation](https://mlsanity.com/paper/2310.17594/) (2023)
- [Minimizing Maximum Model Discrepancy for Transferable Black-box Targeted Attacks](https://mlsanity.com/paper/2212.09035/) (2022)
- [Correspondence-Free Domain Alignment for Unsupervised Cross-Domain Image Retrieval](https://mlsanity.com/paper/2302.06081/) (2023)
- [Gradient Harmonization in Unsupervised Domain Adaptation](https://mlsanity.com/paper/2408.00288/) (2024)
- [Intrapartum Ultrasound Image Segmentation of Pubic Symphysis and Fetal Head Using Dual Student-Teacher Framework with CNN-ViT Collaborative Learning](https://mlsanity.com/paper/2409.06928/) (2024)
- [Exploiting Both Domain-specific and Invariant Knowledge via a Win-win Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2111.12941/) (2021)
- [BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian Perspective](https://mlsanity.com/paper/2202.09541/) (2022)
- [Uncertainty-Aware Pseudo-Label Filtering for Source-Free Unsupervised Domain Adaptation](https://mlsanity.com/paper/2403.11256/) (2024)
- [Delving into the Continuous Domain Adaptation](https://mlsanity.com/paper/2208.13121/) (2022)
- [What Does Softmax Probability Tell Us about Classifiers Ranking Across Diverse Test Conditions?](https://mlsanity.com/paper/2406.09908/) (2024)
- [Dual Classification Head Self-training Network for Cross-scene Hyperspectral Image Classification](https://mlsanity.com/paper/2502.17879/) (2025)
- [Low-confidence Samples Matter for Domain Adaptation](https://mlsanity.com/paper/2202.02802/) (2022)
- [Hard-aware Instance Adaptive Self-training for Unsupervised Cross-domain Semantic Segmentation](https://mlsanity.com/paper/2302.06992/) (2023)
- [Towards Unsupervised Model Selection for Domain Adaptive Object Detection](https://mlsanity.com/paper/2412.17284/) (2024)
- [Reciprocal Normalization for Domain Adaptation](https://mlsanity.com/paper/2112.10474/) (2021)
- [Domain Gap Estimation for Source Free Unsupervised Domain Adaptation with Many Classifiers](https://mlsanity.com/paper/2207.05785/) (2022)
- [DUDA: Distilled Unsupervised Domain Adaptation for Lightweight Semantic Segmentation](https://mlsanity.com/paper/2504.09814/) (2025)
- [Semantic-aware Message Broadcasting for Efficient Unsupervised Domain Adaptation](https://mlsanity.com/paper/2212.02739/) (2022)
- [Incorporating Pre-training Data Matters in Unsupervised Domain Adaptation](https://mlsanity.com/paper/2308.03097/) (2023)
- [SPA++: Generalized Graph Spectral Alignment for Versatile Domain Adaptation](https://mlsanity.com/paper/2508.05182/) (2025)
- [Unsupervised Domain Adaptation with Implicit Pseudo Supervision for Semantic Segmentation](https://mlsanity.com/paper/2204.06747/) (2022)
- [DSTCS: Dual-Student Teacher Framework with Segment Anything Model for Semi-Supervised Pubic Symphysis Fetal Head Segmentation](https://mlsanity.com/paper/2601.19446/) (2026)

## Abstract (arXiv)

Unsupervised domain adaptation challenges the problem of transferring knowledge from a well-labelled source domain to an unlabelled target domain. Recently,adversarial learning with bi-classifier has been proven effective in pushing cross-domain distributions close. Prior approaches typically leverage the disagreement between bi-classifier to learn transferable representations, however, they often neglect the classifier determinacy in the target domain, which could result in a lack of feature discriminability. In this paper, we present a simple yet effective method, namely Bi-Classifier Determinacy Maximization(BCDM), to tackle this problem. Motivated by the observation that target samples cannot always be separated distinctly by the decision boundary, here in the proposed BCDM, we design a novel classifier determinacy disparity (CDD) metric, which formulates classifier discrepancy as the class relevance of distinct target predictions and implicitly introduces constraint on the target feature discriminability. To this end, the BCDM can generate discriminative representations by encouraging target predictive outputs to be consistent and determined, meanwhile, preserve the diversity of predictions in an adversarial manner. Furthermore, the properties of CDD as well as the theoretical guarantees of BCDM's generalization bound are both elaborated. Extensive experiments show that BCDM compares favorably against the existing state-of-the-art domain adaptation methods.