# ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification

arXiv: https://arxiv.org/abs/2401.01448 · HTML: https://mlsanity.com/paper/2401.01448/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 33 references that are in the map.
- First posted on arXiv 2 Jan 2024.

## Builds on (most cited first, 27 of 27)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Supervised Contrastive Learning](https://mlsanity.com/paper/2004.11362/) (2020)
- [Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere](https://mlsanity.com/paper/2005.10242/) (2020)
- [Asymmetric Loss For Multi-Label Classification](https://mlsanity.com/paper/2009.14119/) (2020)
- [Multi-Label Image Recognition with Graph Convolutional Networks](https://mlsanity.com/paper/1904.03582/) (2019)
- [CNN-RNN: A Unified Framework for Multi-label Image Classification](https://mlsanity.com/paper/1604.04573/) (2016)
- [TResNet: High Performance GPU-Dedicated Architecture](https://mlsanity.com/paper/2003.13630/) (2020)
- [Learning Spatial Regularization with Image-level Supervisions for Multi-label Image Classification](https://mlsanity.com/paper/1702.05891/) (2017)
- [DataDAM: Efficient Dataset Distillation with Attention Matching](https://mlsanity.com/paper/2310.00093/) (2023)
- [Multi-Label Classification with Label Graph Superimposing](https://mlsanity.com/paper/1911.09243/) (2019)
- [Use All The Labels: A Hierarchical Multi-Label Contrastive Learning Framework](https://mlsanity.com/paper/2204.13207/) (2022)
- [Residual Attention: A Simple but Effective Method for Multi-Label Recognition](https://mlsanity.com/paper/2108.02456/) (2021)
- [Orderless Recurrent Models for Multi-label Classification](https://mlsanity.com/paper/1911.09996/) (2019)
- [Transformer-based Dual Relation Graph for Multi-label Image Recognition](https://mlsanity.com/paper/2110.04722/) (2021)
- [Mixture Dense Regression for Object Detection and Human Pose Estimation](https://mlsanity.com/paper/1912.00821/) (2019)
- [End-to-End Supervised Multilabel Contrastive Learning](https://mlsanity.com/paper/2307.03967/) (2023)
- [CFDP: Common Frequency Domain Pruning](https://mlsanity.com/paper/2306.04147/) (2023)
- [On the Efficiency of Subclass Knowledge Distillation in Classification Tasks](https://mlsanity.com/paper/2109.05587/) (2021)
- [CONetV2: Efficient Auto-Channel Size Optimization for CNNs](https://mlsanity.com/paper/2110.06830/) (2021)
- [High Performance Convolution Using Sparsity and Patterns for Inference in Deep Convolutional Neural Networks](https://mlsanity.com/paper/2104.08314/) (2021)
- [Subclass Knowledge Distillation with Known Subclass Labels](https://mlsanity.com/paper/2207.08063/) (2022)
- [A New Probabilistic Distance Metric With Application In Gaussian Mixture Reduction](https://mlsanity.com/paper/2306.07309/) (2023)

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

- [The Need for Speed: Pruning Transformers with One Recipe](https://mlsanity.com/paper/2403.17921/) (2024)
- [ATOM: Attention Mixer for Efficient Dataset Distillation](https://mlsanity.com/paper/2405.01373/) (2024)
- [Rethinking Multi-Label Image Classification With Deep Learning: Taxonomy, Challenge, and Outlook](https://mlsanity.com/paper/2607.00839/) (2026)

## Abstract (arXiv)

Multi-label image classification presents a challenging task in many domains, including computer vision and medical imaging. Recent advancements have introduced graph-based and transformer-based methods to improve performance and capture label dependencies. However, these methods often include complex modules that entail heavy computation and lack interpretability. In this paper, we propose Probabilistic Multi-label Contrastive Learning (ProbMCL), a novel framework to address these challenges in multi-label image classification tasks. Our simple yet effective approach employs supervised contrastive learning, in which samples that share enough labels with an anchor image based on a decision threshold are introduced as a positive set. This structure captures label dependencies by pulling positive pair embeddings together and pushing away negative samples that fall below the threshold. We enhance representation learning by incorporating a mixture density network into contrastive learning and generating Gaussian mixture distributions to explore the epistemic uncertainty of the feature encoder. We validate the effectiveness of our framework through experimentation with datasets from the computer vision and medical imaging domains. Our method outperforms the existing state-of-the-art methods while achieving a low computational footprint on both datasets. Visualization analyses also demonstrate that ProbMCL-learned classifiers maintain a meaningful semantic topology.