Summary of Generating Counterfactual Trajectories with Latent Diffusion Models For Concept Discovery, by Payal Varshney et al.
Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery
by Payal Varshney, Adriano Lucieri, Christoph Balada, Andreas Dengel, Sheraz Ahmed
First submitted to arxiv on: 16 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Discovery through Latent Diffusion-based Counterfactual Trajectories (CDCT) framework addresses the challenge of understanding opaque deep learning models’ decision-making processes. CDCT leverages diffusion models for superior image synthesis and combines three steps: generating a counterfactual trajectory dataset using a Latent Diffusion Model (LDM), deriving disentangled representations with a Variational Autoencoder (VAE), and searching for relevant concepts in the latent space. The framework’s application to a skin lesion classification task revealed biases, meaningful biomarkers, and improved FID scores compared to state-of-the-art methods while being more resource-efficient. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary CDCT is a new way to understand how deep learning models make decisions. This helps build trust in these models when they’re used for important tasks like medical diagnosis. The model uses a special technique called diffusion-based counterfactuals to find patterns and concepts that are important for the decision-making process. It’s been tested on a big skin lesion dataset and has shown promising results. |
Keywords
» Artificial intelligence » Classification » Deep learning » Diffusion » Diffusion model » Image synthesis » Latent space » Variational autoencoder
