# ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation

arXiv: https://arxiv.org/abs/2104.09136 · HTML: https://mlsanity.com/paper/2104.09136/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 35 of its 55 references that are in the map.
- First posted on arXiv 19 Apr 2021.

## Builds on (most cited first, 30 of 35)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/1703.05175/) (2017)
- [Matching Networks for One Shot Learning](https://mlsanity.com/paper/1606.04080/) (2016)
- [FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence](https://mlsanity.com/paper/2001.07685/) (2020)
- [Unsupervised Domain Adaptation by Backpropagation](https://mlsanity.com/paper/1409.7495/) (2014)
- [Improved Regularization of Convolutional Neural Networks with Cutout](https://mlsanity.com/paper/1708.04552/) (2017)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Moment Matching for Multi-Source Domain Adaptation](https://mlsanity.com/paper/1812.01754/) (2018)
- [Learning Transferable Features with Deep Adaptation Networks](https://mlsanity.com/paper/1502.02791/) (2015)
- [Deep Hashing Network for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1706.07522/) (2017)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Temporal Ensembling for Semi-Supervised Learning](https://mlsanity.com/paper/1610.02242/) (2016)
- [In Defense of the Triplet Loss for Person Re-Identification](https://mlsanity.com/paper/1703.07737/) (2017)
- [Deep Transfer Learning with Joint Adaptation Networks](https://mlsanity.com/paper/1605.06636/) (2016)
- [VisDA: The Visual Domain Adaptation Challenge](https://mlsanity.com/paper/1710.06924/) (2017)
- [Confidence Regularized Self-Training](https://mlsanity.com/paper/1908.09822/) (2019)
- [Semi-supervised Domain Adaptation via Minimax Entropy](https://mlsanity.com/paper/1904.06487/) (2019)
- [Unified Deep Supervised Domain Adaptation and Generalization](https://mlsanity.com/paper/1709.10190/) (2017)
- [Simultaneous Deep Transfer Across Domains and Tasks](https://mlsanity.com/paper/1510.02192/) (2015)
- [Transferrable Prototypical Networks for Unsupervised Domain Adaptation](https://mlsanity.com/paper/1904.11227/) (2019)
- [Adversarial Feature Hallucination Networks for Few-Shot Learning](https://mlsanity.com/paper/2003.13193/) (2020)
- [Cross-domain Detection via Graph-induced Prototype Alignment](https://mlsanity.com/paper/2003.12849/) (2020)
- [Adversarial Dropout Regularization](https://mlsanity.com/paper/1711.01575/) (2017)
- [Good Semi-supervised Learning that Requires a Bad GAN](https://mlsanity.com/paper/1705.09783/) (2017)
- [Learning to Self-Train for Semi-Supervised Few-Shot Classification](https://mlsanity.com/paper/1906.00562/) (2019)
- [AutoDIAL: Automatic DomaIn Alignment Layers](https://mlsanity.com/paper/1704.08082/) (2017)
- [Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2007.09375/) (2020)
- [Online Meta-Learning for Multi-Source and Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2004.04398/) (2020)

## Built on by (most cited first, 30 of 31)

- [ConfMix: Unsupervised Domain Adaptation for Object Detection via Confidence-based Mixing](https://mlsanity.com/paper/2210.11539/) (2022)
- [Multi-level Consistency Learning for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2205.04066/) (2022)
- [Learning Distinctive Margin toward Active Domain Adaptation](https://mlsanity.com/paper/2203.05738/) (2022)
- [Consistency Regularization for Generalizable Source-free Domain Adaptation](https://mlsanity.com/paper/2308.01587/) (2023)
- [Combating Label Distribution Shift for Active Domain Adaptation](https://mlsanity.com/paper/2208.06604/) (2022)
- [Making Reconstruction-based Method Great Again for Video Anomaly Detection](https://mlsanity.com/paper/2301.12048/) (2023)
- [Semi-supervised Domain Adaptation via Prototype-based Multi-level Learning](https://mlsanity.com/paper/2305.02693/) (2023)
- [Adaptive Betweenness Clustering for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2401.11448/) (2024)
- [Unsupervised Domain Adaptation for Constraining Star Formation Histories](https://mlsanity.com/paper/2112.14072/) (2021)
- [Probabilistic Contrastive Learning for Domain Adaptation](https://mlsanity.com/paper/2111.06021/) (2021)
- [Learning Domain Invariant Representations by Joint Wasserstein Distance Minimization](https://mlsanity.com/paper/2106.04923/) (2021)
- [Low-confidence Samples Matter for Domain Adaptation](https://mlsanity.com/paper/2202.02802/) (2022)
- [Local Context-Aware Active Domain Adaptation](https://mlsanity.com/paper/2208.12856/) (2022)
- [Semi-supervised Domain Adaptation on Graphs with Contrastive Learning and Minimax Entropy](https://mlsanity.com/paper/2309.07402/) (2023)
- [Universal Semi-Supervised Domain Adaptation by Mitigating Common-Class Bias](https://mlsanity.com/paper/2403.11234/) (2024)
- [Domain-Augmented Domain Adaptation](https://mlsanity.com/paper/2202.10000/) (2022)
- [Learning from Different Samples: A Source-free Framework for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2411.06665/) (2024)
- [Multi-Domain Learning From Insufficient Annotations](https://mlsanity.com/paper/2305.02757/) (2023)
- [Universal Semi-supervised Model Adaptation via Collaborative Consistency Training](https://mlsanity.com/paper/2307.03449/) (2023)
- [Dual-level Interaction for Domain Adaptive Semantic Segmentation](https://mlsanity.com/paper/2307.07972/) (2023)
- [AdaEmbed: Semi-supervised Domain Adaptation in the Embedding Space](https://mlsanity.com/paper/2401.12421/) (2024)
- [Adaptive Prompt Learning with Negative Textual Semantics and Uncertainty Modeling for Universal Multi-Source Domain Adaptation](https://mlsanity.com/paper/2404.14696/) (2024)
- [Semi-Supervised Transfer Boosting (SS-TrBoosting)](https://mlsanity.com/paper/2412.03212/) (2024)
- [Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.24567/) (2025)
- [Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling](https://mlsanity.com/paper/2205.13508/) (2022)
- [Pred&Guide: Labeled Target Class Prediction for Guiding Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2211.11975/) (2022)
- [Towards Realizing the Value of Labeled Target Samples: a Two-Stage Approach for Semi-Supervised Domain Adaptation](https://mlsanity.com/paper/2304.10762/) (2023)
- [Progressive Multi-Level Alignments for Semi-Supervised Domain Adaptation SAR Target Recognition Using Simulated Data](https://mlsanity.com/paper/2411.04711/) (2024)
- [Source-free Semantic Regularization Learning for Semi-supervised Domain Adaptation](https://mlsanity.com/paper/2501.01126/) (2025)
- [MoSSDA: A Semi-Supervised Domain Adaptation Framework for Multivariate Time-Series Classification using Momentum Encoder](https://mlsanity.com/paper/2508.08280/) (2025)

## Abstract (arXiv)

This paper studies Semi-Supervised Domain Adaptation (SSDA), a practical yet under-investigated research topic that aims to learn a model of good performance using unlabeled samples and a few labeled samples in the target domain, with the help of labeled samples from a source domain. Several SSDA methods have been proposed recently, which however fail to fully exploit the value of the few labeled target samples. In this paper, we propose Enhanced Categorical Alignment and Consistency Learning (ECACL), a holistic SSDA framework that incorporates multiple mutually complementary domain alignment techniques. ECACL includes two categorical domain alignment techniques that achieve class-level alignment, a strong data augmentation based technique that enhances the model's generalizability and a consistency learning based technique that forces the model to be robust with image perturbations. These techniques are applied on one or multiple of the three inputs (labeled source, unlabeled target, and labeled target) and align the domains from different perspectives. ECACL unifies them together and achieves fairly comprehensive domain alignments that are much better than the existing methods: For example, ECACL raises the state-of-the-art accuracy from 68.4 to 81.1 on VisDA2017 and from 45.5 to 53.4 on DomainNet for the 1-shot setting. Our code is available at urlhttps://github.com/kailigo/pacl.