Summary of Unifying Self-supervised Clustering and Energy-based Models, by Emanuele Sansone and Robin Manhaeve
Unifying Self-Supervised Clustering and Energy-Based Models
by Emanuele Sansone, Robin Manhaeve
First submitted to arxiv on: 30 Dec 2023
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computer Vision and Pattern Recognition (cs.CV)
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| 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 bridges the gap between self-supervised learning and generative models, highlighting their complementary strengths. The authors analyze self-supervised learning objectives using probabilistic graphical models, deriving a standardized methodology for their derivation from first principles. This connection enables the integration of self-supervised learning with likelihood-based generative models. The study introduces a lower bound that reliably penalizes failure modes in cluster-based self-supervised learning and energy models. Experiments on synthetic and real-world data, including SVHN, CIFAR10, and CIFAR100, demonstrate the effectiveness of this approach, outperforming existing self-supervised learning strategies in clustering, generation, and out-of-distribution detection. The paper also shows that the solution can be integrated into a neuro-symbolic framework to tackle the symbol grounding problem. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research combines two ways computers learn: self-supervised learning and generative models. The authors find a connection between these two approaches, which helps them create better models. They analyze how self-supervised learning works using special diagrams, making it easier to combine with generative models. The study shows that this combination can improve performance in clustering, generation, and detecting unusual data. Experiments on real-world datasets confirm the effectiveness of this approach. Additionally, the authors demonstrate how their solution can be used to solve a simple problem related to understanding symbols. |
Keywords
* Artificial intelligence * Clustering * Grounding * Likelihood * Self supervised
