Summary of Explaining An Image Classifier with a Generative Model Conditioned by Uncertainty, By Adrien Lecoz et al.
Explaining an image classifier with a generative model conditioned by uncertainty
by Adrien LeCoz, Stéphane Herbin, Faouzi Adjed
First submitted to arxiv on: 2 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 proposes conditioning a generative model with an image classifier’s uncertainty to analyze and explain its behavior. This is achieved by using the classifier’s output as a latent representation, which is then used to guide the generative process. Preliminary experiments on synthetic data and a corrupted MNIST dataset demonstrate the feasibility of this approach. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper shows how to make a machine learn to create images that match what we want. It does this by using another machine learning model that tells us how sure it is about its predictions. The idea is to use this uncertainty information to make the image creation process more understandable and predictable. So far, it’s been tested on fake data and a special version of the MNIST dataset with mistakes. |
Keywords
» Artificial intelligence » Generative model » Machine learning » Synthetic data
