# Semi-Supervised Learning by Augmented Distribution Alignment

arXiv: https://arxiv.org/abs/1905.08171 · HTML: https://mlsanity.com/paper/1905.08171/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 May 2019.

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

- [Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2305.00673/) (2023)
- [UCC: Uncertainty guided Cross-head Co-training for Semi-Supervised Semantic Segmentation](https://mlsanity.com/paper/2205.10334/) (2022)
- [Semi-Supervised Domain Generalization with Stochastic StyleMatch](https://mlsanity.com/paper/2106.00592/) (2021)
- [Combining Observational and Randomized Data for Estimating Heterogeneous Treatment Effects](https://mlsanity.com/paper/2202.12891/) (2022)
- [MagicNet: Semi-Supervised Multi-Organ Segmentation via Magic-Cube Partition and Recovery](https://mlsanity.com/paper/2212.14310/) (2022)
- [AFAN: Augmented Feature Alignment Network for Cross-Domain Object Detection](https://mlsanity.com/paper/2106.05499/) (2021)
- [The Outcome of the 2022 Landslide4Sense Competition: Advanced Landslide Detection from Multi-Source Satellite Imagery](https://mlsanity.com/paper/2209.02556/) (2022)
- [Constructing and Exploring Intermediate Domains in Mixed Domain Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.08951/) (2024)
- [PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition](https://mlsanity.com/paper/2202.05400/) (2022)
- [Semi-supervised Deep Learning for Image Classification with Distribution Mismatch: A Survey](https://mlsanity.com/paper/2203.00190/) (2022)
- [Distribution Shift Matters for Knowledge Distillation with Webly Collected Images](https://mlsanity.com/paper/2307.11469/) (2023)
- [Learning to Predict Gradients for Semi-Supervised Continual Learning](https://mlsanity.com/paper/2201.09196/) (2022)
- [Dynamic Maintenance of Kernel Density Estimation Data Structure: From Practice to Theory](https://mlsanity.com/paper/2208.03915/) (2022)
- [Uncertainty-aware Evidential Fusion-based Learning for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2404.06177/) (2024)
- [SIAVC: Semi-Supervised Framework for Industrial Accident Video Classification](https://mlsanity.com/paper/2405.14506/) (2024)
- [Revisiting Deep Semi-supervised Learning: An Empirical Distribution Alignment Framework and Its Generalization Bound](https://mlsanity.com/paper/2203.06639/) (2022)
- [FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment](https://mlsanity.com/paper/2311.05168/) (2023)
- [Unleashing the Power of Intermediate Domains for Mixed Domain Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2505.24567/) (2025)
- [Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2607.04249/) (2026)
- [Semi-supervised learning by selective training with pseudo labels via confidence estimation](https://mlsanity.com/paper/2103.08193/) (2021)
- [Taming Overconfident Prediction on Unlabeled Data from Hindsight](https://mlsanity.com/paper/2112.08200/) (2021)
- [Contrastive Credibility Propagation for Reliable Semi-Supervised Learning](https://mlsanity.com/paper/2211.09929/) (2022)
- [Land Cover and Land Use Detection using Semi-Supervised Learning](https://mlsanity.com/paper/2212.11211/) (2022)
- [Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification](https://mlsanity.com/paper/2307.15987/) (2023)
- [Cross-head mutual Mean-Teaching for semi-supervised medical image segmentation](https://mlsanity.com/paper/2310.05082/) (2023)
- [FPMT: Enhanced Semi-Supervised Model for Traffic Incident Detection](https://mlsanity.com/paper/2409.07839/) (2024)
- [DiM: f-Divergence Minimization Guided Sharpness-Aware Optimization for Semi-supervised Medical Image Segmentation](https://mlsanity.com/paper/2411.12350/) (2024)
- [Pairwise Similarity Regularization for Semi-supervised Graph Medical Image Segmentation](https://mlsanity.com/paper/2503.12800/) (2025)
- [Style-Aware Blending and Prototype-Based Cross-Contrast Consistency for Semi-Supervised Medical Image Segmentation](https://mlsanity.com/paper/2507.20729/) (2025)
- [Theoretical Analysis of Measure Consistency Regularization for Partially Observed Data](https://mlsanity.com/paper/2602.01437/) (2026)

## Abstract (arXiv)

In this work, we propose a simple yet effective semi-supervised learning approach called Augmented Distribution Alignment. We reveal that an essential sampling bias exists in semi-supervised learning due to the limited number of labeled samples, which often leads to a considerable empirical distribution mismatch between labeled data and unlabeled data. To this end, we propose to align the empirical distributions of labeled and unlabeled data to alleviate the bias. On one hand, we adopt an adversarial training strategy to minimize the distribution distance between labeled and unlabeled data as inspired by domain adaptation works. On the other hand, to deal with the small sample size issue of labeled data, we also propose a simple interpolation strategy to generate pseudo training samples. Those two strategies can be easily implemented into existing deep neural networks. We demonstrate the effectiveness of our proposed approach on the benchmark SVHN and CIFAR10 datasets. Our code is available at urlhttps://github.com/qinenergy/adanet.