Summary of Automated Classification Of Model Errors on Imagenet, by Momchil Peychev et al.
Automated Classification of Model Errors on ImageNet
by Momchil Peychev, Mark Niklas Müller, Marc Fischer, Martin Vechev
First submitted to arxiv on: 13 Nov 2023
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 research paper explores the limitations of using top-1 accuracy as a measure of progress in computer vision, particularly with the ImageNet dataset. The authors highlight the significant label noise and ambiguity in the dataset, which has driven the development of state-of-the-art models achieving over 95% accuracy. However, this paper proposes new evaluation protocols to investigate why remaining errors persist, shifting the focus from simply measuring top-1 accuracy to understanding the underlying causes. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at how we measure progress in computer vision research. Right now, we use a method called top-1 accuracy, which measures how well an AI model performs on the ImageNet dataset. But there’s a problem – the labels (or names) of some images are wrong or unclear! This makes it hard to tell if models are really improving or not. The researchers propose new ways to test models that go beyond just looking at top-1 accuracy, so we can understand why errors still happen even when models seem very good. |
