Summary of Visual-tcav: Concept-based Attribution and Saliency Maps For Post-hoc Explainability in Image Classification, by Antonio De Santis et al.
Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
by Antonio De Santis, Riccardo Campi, Matteo Bianchi, Marco Brambilla
First submitted to arxiv on: 8 Nov 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 proposes Visual-TCAV, a novel post-hoc explainability framework for Convolutional Neural Networks (CNNs) in image classification tasks. CNNs have achieved remarkable performance gains but operate as black-boxes, raising concerns about transparency and bias mitigation. Existing saliency methods provide local explanations, while concept-based approaches like TCAV offer insights into concept sensitivity, but neither offers a comprehensive understanding of how concepts contribute to predictions or their locations within the input image. Visual-TCAV fills this gap by generating both local and global explanations using Concept Activation Vectors (CAVs) and a generalization of Integrated Gradients. The framework is evaluated on popular CNN architectures and compared to TCAV, demonstrating its validity with known ground truth for explanations. This work contributes to the development of transparent and accountable AI models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research paper creates a new way to understand how computers learn from images. Computers use special tools called Convolutional Neural Networks (CNNs) to classify pictures, but these tools don’t always show us why they made certain decisions. The researchers created a new method called Visual-TCAV that helps explain how CNNs work. This method shows where specific things in an image are recognized by the computer and also shows how important those things are for the computer’s decision. The new method is tested on different types of images and shown to be accurate and helpful. |
Keywords
* Artificial intelligence * Cnn * Generalization * Image classification
