# Implicit Class-Conditioned Domain Alignment for Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/2006.04996 · HTML: https://mlsanity.com/paper/2006.04996/

## Key facts

- Cited by 60 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2020.

## Built on by (most cited first, 30 of 60)

- [Generalized Source-free Domain Adaptation](https://mlsanity.com/paper/2108.01614/) (2021)
- [CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.06165/) (2021)
- [Cycle Self-Training for Domain Adaptation](https://mlsanity.com/paper/2103.03571/) (2021)
- [Prototypical Cross-domain Self-supervised Learning for Few-shot Unsupervised Domain Adaptation](https://mlsanity.com/paper/2103.16765/) (2021)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [f-Domain-Adversarial Learning: Theory and Algorithms](https://mlsanity.com/paper/2106.11344/) (2021)
- [Unsupervised Domain Generalization by Learning a Bridge Across Domains](https://mlsanity.com/paper/2112.02300/) (2021)
- [Neuron Structure Modeling for Generalizable Remote Physiological Measurement](https://mlsanity.com/paper/2303.05955/) (2023)
- [I3Net: Implicit Instance-Invariant Network for Adapting One-Stage Object Detectors](https://mlsanity.com/paper/2103.13757/) (2021)
- [Adversarial Robustness for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.00946/) (2021)
- [Label Shift Adapter for Test-Time Adaptation under Covariate and Label Shifts](https://mlsanity.com/paper/2308.08810/) (2023)
- [Your Data Is Not Perfect: Towards Cross-Domain Out-of-Distribution Detection in Class-Imbalanced Data](https://mlsanity.com/paper/2412.06284/) (2024)
- [CAT: Exploiting Inter-Class Dynamics for Domain Adaptive Object Detection](https://mlsanity.com/paper/2403.19278/) (2024)
- [Relation Matters: Foreground-aware Graph-based Relational Reasoning for Domain Adaptive Object Detection](https://mlsanity.com/paper/2206.02355/) (2022)
- [Nearest Neighborhood-Based Deep Clustering for Source Data-absent Unsupervised Domain Adaptation](https://mlsanity.com/paper/2107.12585/) (2021)
- [Can We Evaluate Domain Adaptation Models Without Target-Domain Labels?](https://mlsanity.com/paper/2305.18712/) (2023)
- [Towards Novel Target Discovery Through Open-Set Domain Adaptation](https://mlsanity.com/paper/2105.02432/) (2021)
- [Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation](https://mlsanity.com/paper/2111.07971/) (2021)
- [AUGCO: Augmentation Consistency-guided Self-training for Source-free Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2107.10140/) (2021)
- [Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency](https://mlsanity.com/paper/2206.08222/) (2022)
- [ALEX: Towards Effective Graph Transfer Learning with Noisy Labels](https://mlsanity.com/paper/2309.14673/) (2023)
- [Boosting Cross-Domain Speech Recognition with Self-Supervision](https://mlsanity.com/paper/2206.09783/) (2022)
- [Trust your Good Friends: Source-free Domain Adaptation by Reciprocal Neighborhood Clustering](https://mlsanity.com/paper/2309.00528/) (2023)
- [Confidence-based Visual Dispersal for Few-shot Unsupervised Domain Adaptation](https://mlsanity.com/paper/2309.15575/) (2023)
- [Graphical Modeling for Multi-Source Domain Adaptation](https://mlsanity.com/paper/2104.13057/) (2021)
- [Multi-source Few-shot Domain Adaptation](https://mlsanity.com/paper/2109.12391/) (2021)
- [Domain Adversarial Training: A Game Perspective](https://mlsanity.com/paper/2202.05352/) (2022)
- [BP-Triplet Net for Unsupervised Domain Adaptation: A Bayesian Perspective](https://mlsanity.com/paper/2202.09541/) (2022)
- [On f-Divergence Principled Domain Adaptation: An Improved Framework](https://mlsanity.com/paper/2402.01887/) (2024)
- [Domain Adaptation with Cauchy-Schwarz Divergence](https://mlsanity.com/paper/2405.19978/) (2024)

## Abstract (arXiv)

We present an approach for unsupervised domain adaptation---with a strong focus on practical considerations of within-domain class imbalance and between-domain class distribution shift---from a class-conditioned domain alignment perspective. Current methods for class-conditioned domain alignment aim to explicitly minimize a loss function based on pseudo-label estimations of the target domain. However, these methods suffer from pseudo-label bias in the form of error accumulation. We propose a method that removes the need for explicit optimization of model parameters from pseudo-labels directly. Instead, we present a sampling-based implicit alignment approach, where the sample selection procedure is implicitly guided by the pseudo-labels. Theoretical analysis reveals the existence of a domain-discriminator shortcut in misaligned classes, which is addressed by the proposed implicit alignment approach to facilitate domain-adversarial learning. Empirical results and ablation studies confirm the effectiveness of the proposed approach, especially in the presence of within-domain class imbalance and between-domain class distribution shift.