Summary of Advancing Ante-hoc Explainable Models Through Generative Adversarial Networks, by Tanmay Garg et al.
Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks
by Tanmay Garg, Deepika Vemuri, Vineeth N Balasubramanian
First submitted to arxiv on: 9 Jan 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 novel framework enhances model interpretability and performance in visual classification tasks by appending an unsupervised explanation generator to the primary classifier network. The approach uses adversarial training, where the explanation module is optimized to extract visual concepts from latent representations, while a GAN-based module discriminates generated images from true ones. This joint training scheme aligns internally learned concepts with human-interpretable visual properties, resulting in coherent concept activations. Comprehensive experiments demonstrate robustness and semantic concordance of learned concepts with object parts and attributes. The study also investigates how perturbations impact classification and concept acquisition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper creates a new way to make machine learning models more understandable and better at recognizing images. It adds a special part to the main model that helps it learn important features, like what makes a cat look like a cat. This part is trained using fake images generated by another AI system, which helps the model understand what those features mean. The results show that this approach works well and helps the model make better predictions. It also shows how the model learns to recognize different parts of an object, like its ears or tail. |
Keywords
* Artificial intelligence * Classification * Gan * Machine learning * Unsupervised
