Summary of Probmcl: Simple Probabilistic Contrastive Learning For Multi-label Visual Classification, by Ahmad Sajedi et al.
ProbMCL: Simple Probabilistic Contrastive Learning for Multi-label Visual Classification
by Ahmad Sajedi, Samir Khaki, Yuri A. Lawryshyn, Konstantinos N. Plataniotis
First submitted to arxiv on: 2 Jan 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper proposes Probabilistic Multi-label Contrastive Learning (ProbMCL), a novel framework for multi-label image classification tasks, particularly in computer vision and medical imaging domains. The approach employs supervised contrastive learning to capture label dependencies by pulling positive pair embeddings together and pushing away negative samples. To enhance representation learning, the authors incorporate a mixture density network into contrastive learning, generating Gaussian mixture distributions to explore epistemic uncertainty of the feature encoder. Experimental results on datasets from both domains show that ProbMCL outperforms existing state-of-the-art methods while maintaining a low computational footprint. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easier for computers to identify images with multiple labels. For example, if you have a picture of a cat and a dog playing together, the computer can recognize both animals in the image. The authors created a new way to do this called Probabilistic Multi-label Contrastive Learning (ProbMCL). They tested their method on pictures from the computer vision and medical imaging domains and found that it worked better than other methods while using less computing power. |
Keywords
* Artificial intelligence * Encoder * Image classification * Representation learning * Supervised
