Summary of On the Universality Of Self-supervised Representation Learning, by Wenwen Qiang et al.
On the Universality of Self-Supervised Representation Learning
by Wenwen Qiang, Jingyao Wang, Lingyu Si, Chuxiong Sun, Fuchun Sun, Hui Xiong
First submitted to arxiv on: 2 May 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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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 the characteristics that define a good representation or model in self-supervised learning (SSL). The authors propose that such a model should possess universality, characterized by discriminability, generalization, and transferability. They argue that current SSL methods lack explicit modeling of universality and theoretical analysis remains underexplored. To address this, they revisit SSL from a task perspective and find that each mini-batch can be viewed as a multi-class classification task. The authors propose two types of universality: learning universality by minimizing loss across all training samples and evaluation universality by learning causally invariant representations that generalize well to unseen tasks. They introduce a σ-measurement to quantify the gap between the performance of SSL models and optimal task-specific models. Furthermore, they propose the GeSSL framework, which learns task-specific models, minimizes SSL loss, and integrates these models to learn from multiple tasks. Theoretical and empirical evidence supports the effectiveness of GeSSL. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In this paper, scientists study what makes a good model in machine learning. They want to know how to create models that can work well on different types of data and tasks. The authors suggest that a good model should be able to perform well on training samples, generalize well to new datasets, and transfer well to new tasks with changes in the data. They also propose a framework called GeSSL that can learn from multiple tasks and improve its performance over time. |
Keywords
» Artificial intelligence » Classification » Generalization » Machine learning » Self supervised » Transferability
