Summary of Adaptive Knowledge Distillation For Classification Of Hand Images Using Explainable Vision Transformers, by Thanh Thi Nguyen et al.
Adaptive Knowledge Distillation for Classification of Hand Images using Explainable Vision Transformers
by Thanh Thi Nguyen, Campbell Wilson, Janis Dalins
First submitted to arxiv on: 20 Aug 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 explores the use of vision transformers (ViTs) for classifying hand images, leveraging their unique features and patterns. By employing explainability tools, researchers investigate how ViTs process information internally and assess its impact on model outputs. A key innovation is the introduction of adaptive distillation methods that enable student models to adapt knowledge from teacher models while learning on data from different domains, preventing catastrophic forgetting. Two publicly available hand image datasets are used in experiments evaluating ViT performance and proposed distillation methods. Results show that ViTs outperform traditional machine learning methods, with internal states useful for explaining model outputs in the classification task. The proposed approaches demonstrate excellent performance on both source and target domains, particularly when these domains exhibit significant dissimilarity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at using special computer models called vision transformers (ViTs) to identify hand images. Hand images have unique patterns that can be used to identify people. The researchers wanted to see how ViTs work and why they’re good for this task. They also came up with a new way to teach these models, so they don’t forget what they learned when shown new information. The researchers tested their ideas using two big collections of hand images. The results show that ViTs are really good at identifying hand images and can even explain why they made certain decisions. |
Keywords
» Artificial intelligence » Classification » Distillation » Machine learning » Vit
