# Combining MixMatch and Active Learning for Better Accuracy with Fewer Labels

arXiv: https://arxiv.org/abs/1912.00594 · HTML: https://mlsanity.com/paper/1912.00594/

## Key facts

- Cited by 17 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2019.

## Built on by (most cited first, 17 of 17)

- [Plex: Towards Reliability using Pretrained Large Model Extensions](https://mlsanity.com/paper/2207.07411/) (2022)
- [BoostMIS: Boosting Medical Image Semi-supervised Learning with Adaptive Pseudo Labeling and Informative Active Annotation](https://mlsanity.com/paper/2203.02533/) (2022)
- [Unsupervised Selective Labeling for More Effective Semi-Supervised Learning](https://mlsanity.com/paper/2110.03006/) (2021)
- [Monocular 3D Object Detection with LiDAR Guided Semi Supervised Active Learning](https://mlsanity.com/paper/2307.08415/) (2023)
- [Data Summarization via Bilevel Optimization](https://mlsanity.com/paper/2109.12534/) (2021)
- [A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity](https://mlsanity.com/paper/2104.03525/) (2021)
- [Consistency-Based Semi-supervised Evidential Active Learning for Diagnostic Radiograph Classification](https://mlsanity.com/paper/2209.01858/) (2022)
- [An Empirical Study on the Efficacy of Deep Active Learning for Image Classification](https://mlsanity.com/paper/2212.03088/) (2022)
- [Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning](https://mlsanity.com/paper/2207.12302/) (2022)
- [Online-PVLM: Advancing Personalized VLMs with Online Concept Learning](https://mlsanity.com/paper/2511.20056/) (2025)
- [Active metric learning and classification using similarity queries](https://mlsanity.com/paper/2202.01953/) (2022)
- [Collaborative Intelligence Orchestration: Inconsistency-Based Fusion of Semi-Supervised Learning and Active Learning](https://mlsanity.com/paper/2206.03288/) (2022)
- [Active Semi-Supervised Learning by Exploring Per-Sample Uncertainty and Consistency](https://mlsanity.com/paper/2303.08978/) (2023)
- [Training Ensembles with Inliers and Outliers for Semi-supervised Active Learning](https://mlsanity.com/paper/2307.03741/) (2023)
- [Enhancing Semi-Supervised Learning via Representative and Diverse Sample Selection](https://mlsanity.com/paper/2409.11653/) (2024)
- [Maximally Separated Active Learning](https://mlsanity.com/paper/2411.17444/) (2024)
- [Balancing Uncertainty and Diversity of Samples: Leveraging Diversity of Least, High Confidence Samples for Effective Active Learning](https://mlsanity.com/paper/2605.22169/) (2026)

## Abstract (arXiv)

We propose using active learning based techniques to further improve the state-of-the-art semi-supervised learning MixMatch algorithm. We provide a thorough empirical evaluation of several active-learning and baseline methods, which successfully demonstrate a significant improvement on the benchmark CIFAR-10, CIFAR-100, and SVHN datasets (as much as 1.5% in absolute accuracy). We also provide an empirical analysis of the cost trade-off between incrementally gathering more labeled versus unlabeled data. This analysis can be used to measure the relative value of labeled/unlabeled data at different points of the learning curve, where we find that although the incremental value of labeled data can be as much as 20x that of unlabeled, it quickly diminishes to less than 3x once more than 2,000 labeled example are observed. Code can be found at https://github.com/google-research/mma.