# Mutual Exclusivity Loss for Semi-Supervised Deep Learning

arXiv: https://arxiv.org/abs/1606.03141 · HTML: https://mlsanity.com/paper/1606.03141/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jun 2016.

## Built on by (most cited first, 22 of 22)

- [Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation](https://mlsanity.com/paper/2207.14191/) (2022)
- [Semi-Supervised and Unsupervised Deep Visual Learning: A Survey](https://mlsanity.com/paper/2208.11296/) (2022)
- [Boosting Semi-Supervised Learning by Exploiting All Unlabeled Data](https://mlsanity.com/paper/2303.11066/) (2023)
- [ConMatch: Semi-Supervised Learning with Confidence-Guided Consistency Regularization](https://mlsanity.com/paper/2208.08631/) (2022)
- [Rethinking Re-Sampling in Imbalanced Semi-Supervised Learning](https://mlsanity.com/paper/2106.00209/) (2021)
- [Hierarchical Deep Network with Uncertainty-aware Semi-supervised Learning for Vessel Segmentation](https://mlsanity.com/paper/2105.14732/) (2021)
- [MarginMatch: Improving Semi-Supervised Learning with Pseudo-Margins](https://mlsanity.com/paper/2308.09037/) (2023)
- [Semi-Supervised Text Classification via Self-Pretraining](https://mlsanity.com/paper/2109.15300/) (2021)
- [Evaluating Self and Semi-Supervised Methods for Remote Sensing Segmentation Tasks](https://mlsanity.com/paper/2111.10079/) (2021)
- [Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption](https://mlsanity.com/paper/2310.13434/) (2023)
- [Revisiting consistency for semi-supervised semantic segmentation](https://mlsanity.com/paper/2106.07075/) (2021)
- [Labels, Information, and Computation: Efficient Learning Using Sufficient Labels](https://mlsanity.com/paper/2104.09015/) (2021)
- [Colour augmentation for improved semi-supervised semantic segmentation](https://mlsanity.com/paper/2110.04487/) (2021)
- [Deep Reference Priors: What is the best way to pretrain a model?](https://mlsanity.com/paper/2202.00187/) (2022)
- [Combating Confirmation Bias: A Unified Pseudo-Labeling Framework for Entity Alignment](https://mlsanity.com/paper/2307.02075/) (2023)
- [AggMatch: Aggregating Pseudo Labels for Semi-Supervised Learning](https://mlsanity.com/paper/2201.10444/) (2022)
- [Complementing Semi-Supervised Learning with Uncertainty Quantification](https://mlsanity.com/paper/2207.12131/) (2022)
- [Decoupled Entropy Minimization](https://mlsanity.com/paper/2511.03256/) (2025)
- [BPLF: A Bi-Parallel Linear Flow Model for Facial Expression Generation from Emotion Set Images](https://mlsanity.com/paper/2106.07563/) (2021)
- [Penalizing Proposals using Classifiers for Semi-Supervised Object Detection](https://mlsanity.com/paper/2205.13219/) (2022)
- ['Labelling the Gaps': A Weakly Supervised Automatic Eye Gaze Estimation](https://mlsanity.com/paper/2208.01840/) (2022)
- [Feature Space Renormalization for Semi-supervised Learning](https://mlsanity.com/paper/2311.04055/) (2023)

## Abstract (arXiv)

In this paper we consider the problem of semi-supervised learning with deep Convolutional Neural Networks (ConvNets). Semi-supervised learning is motivated on the observation that unlabeled data is cheap and can be used to improve the accuracy of classifiers. In this paper we propose an unsupervised regularization term that explicitly forces the classifier's prediction for multiple classes to be mutually-exclusive and effectively guides the decision boundary to lie on the low density space between the manifolds corresponding to different classes of data. Our proposed approach is general and can be used with any backpropagation-based learning method. We show through different experiments that our method can improve the object recognition performance of ConvNets using unlabeled data.