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About A Survey On Semi-, Self - and Unsupervised Learning For Image Classification
This survey provides an overview of image classification methods that require fewer labeled examples than traditional supervised deep learning approaches. It analyzes 34 recent semi-supervised, self-supervised, and unsupervised methods and identifies three major trends: 1) methods can scale to real-world applications but do not address issues like class imbalance; 2) the amount of supervision needed is decreasing; 3) methods share common ideas but could benefit from combining ideas across different approaches. The survey aims to help researchers keep track of developments in using unlabeled data to improve image classification.
Detailed Information
| Author: | ['Li Jian'] |
|---|---|
| Publication Year: | 2021 |
| Pages: | 33 |
| Language: | English |
| Format: | |
| Price: | FREE |
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