Summary of Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning, by Andrei Semenov et al.
Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning
by Andrei Semenov, Vladimir Ivanov, Aleksandr Beznosikov, Alexander Gasnikov
First submitted to arxiv on: 4 Apr 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 Concept Bottleneck Model (CBM) architecture and method provide explainable classification results for the Image Classification task. While current approaches work as black boxes, there is a growing demand for models that can provide interpreted results. Existing Bottleneck methods have limitations, such as lower accuracy than standard models and requiring additional concept sets to leverage. The framework creates CBMs from pre-trained multi-modal encoders and new CLIP-like architectures using Concept Bottleneck Layers, trained with _1-loss, contrastive loss, or Gumbel-Softmax distribution-based loss (Sparse-CBM). This approach shows a significant increase in accuracy using sparse hidden layers in CLIP-based bottleneck models. The Concept Matrix Search algorithm improves CLIP predictions on complex datasets without additional training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper proposes an innovative way to make image classification more understandable by creating a model that provides explanations for its results. Current models are like black boxes, but this new approach uses something called Concept Bottleneck Models (CBMs) to provide interpreted results. The CBMs use special layers and training methods to achieve better accuracy while still providing explanations. This breakthrough can help improve our understanding of how image classification works. |
Keywords
» Artificial intelligence » Classification » Contrastive loss » Image classification » Multi modal » Softmax
