Summary of Class-aware and Augmentation-free Contrastive Learning From Label Proportion, by Jialiang Wang et al.
Class-aware and Augmentation-free Contrastive Learning from Label Proportion
by Jialiang Wang, Ning Zhang, Shimin Di, Ruidong Wang, Lei Chen
First submitted to arxiv on: 13 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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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 addresses a key challenge in weakly supervised learning known as Learning from Label Proportion (LLP). In LLP, data is organized into predefined bags with only the class label proportions disclosed. This scenario is crucial for user modeling and personalization, where individual data privacy must be maintained while still gaining insights into user preferences. The paper highlights the difficulty in aligning bag-level supervision with instance-level prediction due to ambiguity in label proportion matching. While deep representation learning has shown promise in promoting supervision levels in image domains, applying these techniques to tabular data presents significant challenges due to heterogeneous datasets and limited semantics for perfect class distinction. The authors aim to develop effective methods to tackle these challenges. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a special kind of learning called Learning from Label Proportion (LLP). In LLP, we have groups of things with only the proportion of good or bad examples told, not individual details. This helps us learn about people’s preferences without knowing who they are. The problem is that it’s hard to connect these group labels to single item predictions because there can be multiple ways to match labels. People have tried using deep learning techniques to help, but this works best for pictures and not as well for tables of data. The authors want to find new solutions to overcome these challenges. |
Keywords
» Artificial intelligence » Deep learning » Representation learning » Semantics » Supervised
