# Confidence-Guided Data Augmentation for Improved Semi-Supervised Training

arXiv: https://arxiv.org/abs/2209.08174 · HTML: https://mlsanity.com/paper/2209.08174/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 19 references that are in the map.
- First posted on arXiv 16 Sep 2022.

## Builds on (most cited first, 12 of 12)

- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [mixup: Beyond Empirical Risk Minimization](https://mlsanity.com/paper/1710.09412/) (2017)
- [Auto-Encoding Variational Bayes](https://mlsanity.com/paper/1312.6114/) (2013)
- [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://mlsanity.com/paper/1905.04899/) (2019)
- [MixMatch: A Holistic Approach to Semi-Supervised Learning](https://mlsanity.com/paper/1905.02249/) (2019)
- [Measuring Robustness to Natural Distribution Shifts in Image Classification](https://mlsanity.com/paper/2007.00644/) (2020)
- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
- [SnapMix: Semantically Proportional Mixing for Augmenting Fine-grained Data](https://mlsanity.com/paper/2012.04846/) (2020)
- [Cardiac Segmentation with Strong Anatomical Guarantees](https://mlsanity.com/paper/2006.08825/) (2020)
- [DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification](https://mlsanity.com/paper/1809.00981/) (2018)
- [Counterexample-Guided Data Augmentation](https://mlsanity.com/paper/1805.06962/) (2018)
- [Lung Segmentation from Chest X-rays using Variational Data Imputation](https://mlsanity.com/paper/2005.10052/) (2020)

## Abstract (arXiv)

We propose a new strategy to improve the accuracy and robustness of image classification. First, we train a baseline CNN model. Then, we identify challenging regions in the feature space by identifying all misclassified samples, and correctly classified samples with low confidence values. These samples are then used to train a Variational AutoEncoder (VAE). Next, the VAE is used to generate synthetic images. Finally, the generated synthetic images are used in conjunction with the original labeled images to train a new model in a semi-supervised fashion. Empirical results on benchmark datasets such as STL10 and CIFAR-100 show that the synthetically generated samples can further diversify the training data, leading to improvement in image classification in comparison with the fully supervised baseline approaches using only the available data.