Summary of Guess: Generative Uncertainty Ensemble For Self Supervision, by Salman Mohamadi et al.
GUESS: Generative Uncertainty Ensemble for Self Supervision
by Salman Mohamadi, Gianfranco Doretto, Donald A. Adjeroh
First submitted to arxiv on: 3 Dec 2024
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 investigates self-supervised learning (SSL) frameworks that learn general features from unlabeled data without explicit supervision. The authors identify an issue with existing SSL baselines, which enforce invariance to data augmentations in a deterministic way, potentially hindering performance on downstream tasks. To address this, the authors propose incorporating uncertainty representation into both loss functions and architecture designs. Specifically, they introduce a new approach called GUESS (a pseudo-whitening framework) that involves controlled uncertainty injection, a novel architecture, and a new loss function. The authors provide detailed results and ablation analysis to establish GUESS as a new baseline for SSL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about learning from data without labels. It’s called self-supervised learning (SSL). The problem with current methods is that they try to make sure the learned features are not changed by random transformations of the data, which can actually hurt performance on other tasks. To fix this, the authors suggest making sure the model is unsure about what it’s learning, and then using this uncertainty to make better decisions. They introduce a new approach called GUESS that does just this. The results show that GUESS performs well and could be used as a baseline for future research. |
Keywords
» Artificial intelligence » Loss function » Self supervised
