Summary of Neuron Abandoning Attention Flow: Visual Explanation Of Dynamics Inside Cnn Models, by Yi Liao et al.
Neuron Abandoning Attention Flow: Visual Explanation of Dynamics inside CNN Models
by Yi Liao, Yongsheng Gao, Weichuan Zhang
First submitted to arxiv on: 2 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG); Multimedia (cs.MM)
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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 paper presents a novel attention flow method called Neuron Abandoning Attention Flow (NAFlow) to explain visually the attention dynamics inside convolutional neural networks (CNNs). The NAFlow method involves a cascading neuron abandoning back-propagation algorithm that traces neurons in all layers of the CNN, abandoning those not involved in making predictions. This approach generates feature maps and importance coefficients used to visualize attention flow for similarity metric-based CNN models. The proposed method is validated on nine widely-used CNN models for various tasks such as image classification, contrastive learning, few-shot image classification, and image retrieval. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary NAFlow is a new way to understand how convolutional neural networks (CNNs) make decisions. It’s like tracing the path that neurons take when they’re not used in making predictions. This helps us visualize attention flow inside CNNs, which is important for many applications like image classification and image retrieval. |
Keywords
» Artificial intelligence » Attention » Cnn » Few shot » Image classification
