# Pseudo-Labeling Curriculum for Unsupervised Domain Adaptation

arXiv: https://arxiv.org/abs/1908.00262 · HTML: https://mlsanity.com/paper/1908.00262/

## Key facts

- Cited by 46 papers in the map (data through 7 Oct 2026).
- First posted on arXiv 1 Aug 2019.

## Built on by (most cited first, 30 of 46)

- [Curriculum Learning: A Survey](https://mlsanity.com/paper/2101.10382/) (2021)
- [MIC: Masked Image Consistency for Context-Enhanced Domain Adaptation](https://mlsanity.com/paper/2212.01322/) (2022)
- [ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection](https://mlsanity.com/paper/2103.05346/) (2021)
- [CDTrans: Cross-domain Transformer for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2109.06165/) (2021)
- [GANcraft: Unsupervised 3D Neural Rendering of Minecraft Worlds](https://mlsanity.com/paper/2104.07659/) (2021)
- [Cross-Domain Gradient Discrepancy Minimization for Unsupervised Domain Adaptation](https://mlsanity.com/paper/2106.04151/) (2021)
- [ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection](https://mlsanity.com/paper/2108.06682/) (2021)
- [C-SFDA: A Curriculum Learning Aided Self-Training Framework for Efficient Source Free Domain Adaptation](https://mlsanity.com/paper/2303.17132/) (2023)
- [CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual Grounding](https://mlsanity.com/paper/2305.08685/) (2023)
- [CALDA: Improving Multi-Source Time Series Domain Adaptation with Contrastive Adversarial Learning](https://mlsanity.com/paper/2109.14778/) (2021)
- [Multiple Instance Learning via Iterative Self-Paced Supervised Contrastive Learning](https://mlsanity.com/paper/2210.09452/) (2022)
- [FHIST: A Benchmark for Few-shot Classification of Histological Images](https://mlsanity.com/paper/2206.00092/) (2022)
- [Domain Generalization via Balancing Training Difficulty and Model Capability](https://mlsanity.com/paper/2309.00844/) (2023)
- [On-target Adaptation](https://mlsanity.com/paper/2109.01087/) (2021)
- [CLIP-Powered Domain Generalization and Domain Adaptation: A Comprehensive Survey](https://mlsanity.com/paper/2504.14280/) (2025)
- [DARCNN: Domain Adaptive Region-based Convolutional Neural Network for Unsupervised Instance Segmentation in Biomedical Images](https://mlsanity.com/paper/2104.01325/) (2021)
- [Few Shots Are All You Need: A Progressive Few Shot Learning Approach for Low Resource Handwritten Text Recognition](https://mlsanity.com/paper/2107.10064/) (2021)
- [Learning to Learn: How to Continuously Teach Humans and Machines](https://mlsanity.com/paper/2211.15470/) (2022)
- [Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery](https://mlsanity.com/paper/2104.14032/) (2021)
- [Effective Pseudo-Labeling based on Heatmap for Unsupervised Domain Adaptation in Cell Detection](https://mlsanity.com/paper/2303.05269/) (2023)
- [Curriculum-Based Augmented Fourier Domain Adaptation for Robust Medical Image Segmentation](https://mlsanity.com/paper/2306.03511/) (2023)
- [SRoUDA: Meta Self-training for Robust Unsupervised Domain Adaptation](https://mlsanity.com/paper/2212.05917/) (2022)
- [Digital Twin-based Anomaly Detection with Curriculum Learning in Cyber-physical Systems](https://mlsanity.com/paper/2309.15995/) (2023)
- [Progressive Multi-Source Domain Adaptation for Personalized Facial Expression Recognition](https://mlsanity.com/paper/2504.04252/) (2025)
- [Training-Free Unsupervised Prompt for Vision-Language Models](https://mlsanity.com/paper/2404.16339/) (2024)
- [CLIP the Divergence: Language-guided Unsupervised Domain Adaptation](https://mlsanity.com/paper/2407.01842/) (2024)
- [DUDA: Distilled Unsupervised Domain Adaptation for Lightweight Semantic Segmentation](https://mlsanity.com/paper/2504.09814/) (2025)
- [Towards Adaptive Unknown Authentication for Universal Domain Adaptation by Classifier Paradox](https://mlsanity.com/paper/2207.04494/) (2022)
- [Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving](https://mlsanity.com/paper/2210.08748/) (2022)
- [Self-Paced Learning for Open-Set Domain Adaptation](https://mlsanity.com/paper/2303.05933/) (2023)

## Abstract (arXiv)

To learn target discriminative representations, using pseudo-labels is a simple yet effective approach for unsupervised domain adaptation. However, the existence of false pseudo-labels, which may have a detrimental influence on learning target representations, remains a major challenge. To overcome this issue, we propose a pseudo-labeling curriculum based on a density-based clustering algorithm. Since samples with high density values are more likely to have correct pseudo-labels, we leverage these subsets to train our target network at the early stage, and utilize data subsets with low density values at the later stage. We can progressively improve the capability of our network to generate pseudo-labels, and thus these target samples with pseudo-labels are effective for training our model. Moreover, we present a clustering constraint to enhance the discriminative power of the learned target features. Our approach achieves state-of-the-art performance on three benchmarks: Office-31, imageCLEF-DA, and Office-Home.