Summary of Understanding Visual Feature Reliance Through the Lens Of Complexity, by Thomas Fel et al.
Understanding Visual Feature Reliance through the Lens of Complexity
by Thomas Fel, Louis Bethune, Andrew Kyle Lampinen, Thomas Serre, Katherine Hermann
First submitted to arxiv on: 8 Jul 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 paper introduces a novel metric for quantifying feature complexity in deep learning models, which could help understand shortcut learning biases. The -information metric captures whether features require complex computational transformations to be extracted and is applied to analyze 10,000 features from an ImageNet-trained vision model. The study finds that simpler features dominate early training, with more complex features emerging gradually; simple features tend to bypass the visual hierarchy via residual connections; and complex features are less important in driving network decisions. These findings suggest that models prioritize simple features for decision-making. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper explores how deep learning models learn and use different types of features from images. It introduces a new way to measure how complex these features are, which helps us understand why some features might be more important than others. The study finds that the model starts by using simple features and then gradually adds more complex ones as it learns. It also shows where in the network these features flow and which ones are most important for making decisions. |
Keywords
» Artificial intelligence » Deep learning
