# Adversarial Feature Distribution Alignment for Semi-Supervised Learning

arXiv: https://arxiv.org/abs/1912.10428 · HTML: https://mlsanity.com/paper/1912.10428/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Dec 2019.

## Built on by (most cited first, 3 of 3)

- [Deep Unified Representation for Heterogeneous Recommendation](https://mlsanity.com/paper/2201.05861/) (2022)
- [ST-DAI: Single-shot 2.5D Spatial Transcriptomics with Intra-Sample Domain Adaptive Imputation for Cost-efficient 3D Reconstruction](https://mlsanity.com/paper/2507.21516/) (2025)
- [Land Cover and Land Use Detection using Semi-Supervised Learning](https://mlsanity.com/paper/2212.11211/) (2022)

## Abstract (arXiv)

Training deep neural networks with only a few labeled samples can lead to overfitting. This is problematic in semi-supervised learning where only a few labeled samples are available. In this paper, we show that a consequence of overfitting in SSL is feature distribution misalignment between labeled and unlabeled samples. Hence, we propose a new feature distribution alignment method. Our method is particularly effective when using only a small amount of labeled samples. We test our method on CIFAR10 and SVHN. On SVHN we achieve a test error of 3.88% (250 labeled samples) and 3.39% (1000 labeled samples) which is close to the fully supervised model 2.89% (73k labeled samples). In comparison, the current SOTA achieves only 4.29% and 3.74%. Finally, we provide a theoretical insight why feature distribution alignment occurs and show that our method reduces it.