Summary of Energy Score-based Pseudo-label Filtering and Adaptive Loss For Imbalanced Semi-supervised Sar Target Recognition, by Xinzheng Zhang et al.
Energy Score-based Pseudo-Label Filtering and Adaptive Loss for Imbalanced Semi-supervised SAR target recognition
by Xinzheng Zhang, Yuqing Luo, Guopeng Li
First submitted to arxiv on: 6 Nov 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 This paper presents a new approach to synthetic aperture radar (SAR) automatic target recognition (ATR), which addresses the issue of class imbalance in semi-supervised learning. The proposed method uses dynamic energy scores and adaptive loss functions to recognize targets with high accuracy, even when there is a significant difference in the number of samples between classes. This is achieved by developing an energy score-based method that selects pseudo-labels near the training distribution, ensuring reliable pseudo-label generation for long-tailed distributions. Additionally, the paper proposes two novel loss functions: adaptive margin perception loss and adaptive hard triplet loss. The former offsets inter-class confusion of classifiers, while the latter focuses on complex difficult samples during training to alleviate model bias caused by data imbalance. Experimental results demonstrate that the proposed method performs well under dual constraints of scarce labels and data imbalance, achieving high-precision target recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us recognize objects in pictures taken from space using radar waves. It’s hard because some types of objects are much more common than others, making it difficult for machines to learn what they look like. The authors found a way to make the machine learning model work better by using special math tricks and choosing which samples to use during training. This helps the model focus on the less common objects, so it can recognize them accurately. The results show that this new approach is very good at recognizing objects in space images. |
Keywords
» Artificial intelligence » Machine learning » Precision » Semi supervised » Triplet loss
