Summary of Fsl-rectifier: Rectify Outliers in Few-shot Learning Via Test-time Augmentation, by Yunwei Bai et al.
FSL-Rectifier: Rectify Outliers in Few-Shot Learning via Test-Time Augmentation
by Yunwei Bai, Ying Kiat Tan, Shiming Chen, Yao Shu, Tsuhan Chen
First submitted to arxiv on: 28 Feb 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 novel approach to improve the generalization capability of few-shot learning (FSL) models, which is crucial for real-world applications. The authors propose a generative image combiner that combines original samples with suitable train-class samples to generate additional test-class samples. This method reduces the bias caused by outlier queries or support images during inference. The authors experimentally and theoretically demonstrate the effectiveness of their method, achieving a significant improvement in test accuracy (around 10%) for trained FSL models. Importantly, this approach is training-free and can be applied to off-the-shelf FSL models without requiring additional datasets or further training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary In simple terms, this paper helps machines learn new things from just a few examples by combining old and new information in a clever way. This makes the machine learning process more accurate and reliable. The idea is to reduce mistakes caused by unusual data during testing. By doing so, it improves the overall performance of the model. |
Keywords
* Artificial intelligence * Few shot * Generalization * Inference * Machine learning
