Summary of Free Performance Gain From Mixing Multiple Partially Labeled Samples in Multi-label Image Classification, by Chak Fong Chong et al.
Free Performance Gain from Mixing Multiple Partially Labeled Samples in Multi-label Image Classification
by Chak Fong Chong, Jielong Guo, Xu Yang, Wei Ke, Yapeng Wang
First submitted to arxiv on: 24 May 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 The proposed LogicMix is a Mixup variant designed for partially labeled multi-label image classification datasets, which allows the mixing of unknown labels using logical OR operations. This approach constructs visually more confused augmented samples, regularizing training and improving model performance. LogicMix is more general and effective than other compared variants in various experiments on partially labeled dataset scenarios. It can be easily inserted into existing frameworks to collaborate with other methods without significant computational overhead. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LogicMix helps train better deep classifiers for partially labeled image datasets by mixing unknown labels using logical OR operations. This makes the training process more robust and accurate. The approach is simple, efficient, and works well with other training techniques, like RandAugment and Curriculum Labeling. By combining LogicMix with these methods, researchers can achieve state-of-the-art performance on popular benchmark datasets. |
Keywords
» Artificial intelligence » Image classification
