Summary of Dataset Awareness Is Not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning, by Haowen Xiao et al.
Dataset Awareness is not Enough: Implementing Sample-level Tail Encouragement in Long-tailed Self-supervised Learning
by Haowen Xiao, Guanghui Liu, Xinyi Gao, Yang Li, Fengmao Lv, Jielei Chu
First submitted to arxiv on: 30 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- 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 In a self-supervised learning setting, researchers have found that applying temperature mechanisms or category-space uniformity constraints can improve data representation capabilities. However, these methods often focus on optimizing the entire dataset or constraining the category distribution, neglecting individual sample guidance during training. To address this issue, we propose Temperature Auxiliary Sample-level Encouragement (TASE), which utilizes pseudo-labels to drive a dynamic temperature and re-weighting strategy. Our approach assigns an optimal temperature parameter to each sample, compensating for quantity awareness deficiency through re-weighting. Comprehensive experiments on six benchmarks across three datasets demonstrate that TASE achieves outstanding performance in improving long-tail recognition while showcasing high robustness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Self-supervised learning is a way for computers to learn from data without being told exactly what to do. It’s like teaching a child to recognize pictures of cats and dogs by showing them many examples, without saying which one is a cat or dog. This method has been very good at recognizing different types of things in pictures. But when the pictures are not equally common, it gets worse at recognizing some things. To fix this, we came up with a new way to use self-supervised learning that pays attention to each individual picture and makes sure it’s being taught correctly. We tested our method on many different kinds of data and found that it works really well and is very good at recognizing things, even if they’re not common. |
Keywords
» Artificial intelligence » Attention » Self supervised » Temperature
