Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering

• URL: http://arxiv.org/abs/2505.07073v1
• Date: Sun, 11 May 2025 17:53:02 GMT
• Title: Discovering Concept Directions from Diffusion-based Counterfactuals via Latent Clustering
• Authors: Payal Varshney, Adriano Lucieri, Christoph Balada, Andreas Dengel, Sheraz Ahmed,
• Abstract summary: Concept-based explanations have emerged as an effective approach within Explainable Artificial Intelligence.<n>This work introduces Concept Directions via Latent Clustering (CDLC), which extracts global, class-specific concept directions.<n>This approach is validated on a real-world skin lesion dataset.
• Score: 4.891597567642704
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Concept-based explanations have emerged as an effective approach within Explainable Artificial Intelligence, enabling interpretable insights by aligning model decisions with human-understandable concepts. However, existing methods rely on computationally intensive procedures and struggle to efficiently capture complex, semantic concepts. Recently, the Concept Discovery through Latent Diffusion-based Counterfactual Trajectories (CDCT) framework, introduced by Varshney et al. (2025), attempts to identify concepts via dimension-wise traversal of the latent space of a Variational Autoencoder trained on counterfactual trajectories. Extending the CDCT framework, this work introduces Concept Directions via Latent Clustering (CDLC), which extracts global, class-specific concept directions by clustering latent difference vectors derived from factual and diffusion-generated counterfactual image pairs. CDLC substantially reduces computational complexity by eliminating the exhaustive latent dimension traversal required in CDCT and enables the extraction of multidimensional semantic concepts encoded across the latent dimensions. This approach is validated on a real-world skin lesion dataset, demonstrating that the extracted concept directions align with clinically recognized dermoscopic features and, in some cases, reveal dataset-specific biases or unknown biomarkers. These results highlight that CDLC is interpretable, scalable, and applicable across high-stakes domains and diverse data modalities.

Related papers

• Concept-Guided Interpretability via Neural Chunking [54.73787666584143]
  We show that neural networks exhibit patterns in their raw population activity that mirror regularities in the training data.<n>We propose three methods to extract these emerging entities, complementing each other based on label availability and dimensionality.<n>Our work points to a new direction for interpretability, one that harnesses both cognitive principles and the structure of naturalistic data.
  arXiv  Detail & Related papers  (2025-05-16T13:49:43Z)
• I Predict Therefore I Am: Is Next Token Prediction Enough to Learn Human-Interpretable Concepts from Data? [76.15163242945813]
  Large language models (LLMs) have led many to conclude that they exhibit a form of intelligence.<n>We introduce a novel generative model that generates tokens on the basis of human-interpretable concepts represented as latent discrete variables.
  arXiv  Detail & Related papers  (2025-03-12T01:21:17Z)
• Post-Hoc Concept Disentanglement: From Correlated to Isolated Concept Representations [12.072112471560716]
  Concept Activation Vectors (CAVs) are widely used to model human-understandable concepts.<n>They are trained by identifying directions from the activations of concept samples to those of non-concept samples.<n>This method produces similar, non-orthogonal directions for correlated concepts, such as "beard" and "necktie"<n>This entanglement complicates the interpretation of concepts in isolation and can lead to undesired effects in CAV applications.
  arXiv  Detail & Related papers  (2025-03-07T15:45:43Z)
• Discover-then-Name: Task-Agnostic Concept Bottlenecks via Automated Concept Discovery [52.498055901649025]
  Concept Bottleneck Models (CBMs) have been proposed to address the 'black-box' problem of deep neural networks. We propose a novel CBM approach -- called Discover-then-Name-CBM (DN-CBM) -- that inverts the typical paradigm. Our concept extraction strategy is efficient, since it is agnostic to the downstream task, and uses concepts already known to the model.
  arXiv  Detail & Related papers  (2024-07-19T17:50:11Z)
• Generating Counterfactual Trajectories with Latent Diffusion Models for Concept Discovery [4.891597567642704]
  This study proposes Concept Discovery through Latent Diffusion-based Counterfactual Trajectories (CDCT)<n>CDCT is a novel three-step framework for concept discovery leveraging the superior image synthesis capabilities of diffusion models.<n>The application of CDCT to a trained on the largest public skin lesion dataset revealed not only the presence of several biases but also meaningful biomarkers.
  arXiv  Detail & Related papers  (2024-04-16T07:44:08Z)
• Understanding Distributed Representations of Concepts in Deep Neural Networks without Supervision [25.449397570387802]
  We propose an unsupervised method for discovering distributed representations of concepts by selecting a principal subset of neurons. Our empirical findings demonstrate that instances with similar neuron activation states tend to share coherent concepts. It can be utilized to identify unlabeled subclasses within data and to detect the causes of misclassifications.
  arXiv  Detail & Related papers  (2023-12-28T07:33:51Z)
• Uncovering Unique Concept Vectors through Latent Space Decomposition [0.0]
  Concept-based explanations have emerged as a superior approach that is more interpretable than feature attribution estimates. We propose a novel post-hoc unsupervised method that automatically uncovers the concepts learned by deep models during training. Our experiments reveal that the majority of our concepts are readily understandable to humans, exhibit coherency, and bear relevance to the task at hand.
  arXiv  Detail & Related papers  (2023-07-13T17:21:54Z)
• Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations [53.973055975918655]
  We show that, intermediate layer representations become more interpretable when transformed to the bases extracted with our method. We compare the bases extracted with our method with the bases derived with a supervised approach and find that, in one aspect, the proposed unsupervised approach has a strength that constitutes a limitation of the supervised one and give potential directions for future research.
  arXiv  Detail & Related papers  (2023-03-19T00:37:19Z)
• Concept Activation Regions: A Generalized Framework For Concept-Based Explanations [95.94432031144716]
  Existing methods assume that the examples illustrating a concept are mapped in a fixed direction of the deep neural network's latent space. In this work, we propose allowing concept examples to be scattered across different clusters in the DNN's latent space. This concept activation region (CAR) formalism yields global concept-based explanations and local concept-based feature importance.
  arXiv  Detail & Related papers  (2022-09-22T17:59:03Z)
• Discovering Concepts in Learned Representations using Statistical Inference and Interactive Visualization [0.76146285961466]
  Concept discovery is important for bridging the gap between non-deep learning experts and model end-users. Current approaches include hand-crafting concept datasets and then converting them to latent space directions. In this study, we offer another two approaches to guide user discovery of meaningful concepts, one based on multiple hypothesis testing, and another on interactive visualization.
  arXiv  Detail & Related papers  (2022-02-09T22:29:48Z)
• Navigating Neural Space: Revisiting Concept Activation Vectors to Overcome Directional Divergence [13.618809162030486]
  Concept Activation Vectors (CAVs) have emerged as a popular tool for modeling human-understandable concepts in the latent space.<n>In this paper we show that such a separability-oriented leads to solutions, which may diverge from the actual goal of precisely modeling the concept direction.<n>We introduce pattern-based CAVs, solely focussing on concept signals, thereby providing more accurate concept directions.
  arXiv  Detail & Related papers  (2022-02-07T19:40:20Z)
• Closed-Form Factorization of Latent Semantics in GANs [65.42778970898534]
  A rich set of interpretable dimensions has been shown to emerge in the latent space of the Generative Adversarial Networks (GANs) trained for synthesizing images. In this work, we examine the internal representation learned by GANs to reveal the underlying variation factors in an unsupervised manner. We propose a closed-form factorization algorithm for latent semantic discovery by directly decomposing the pre-trained weights.
  arXiv  Detail & Related papers  (2020-07-13T18:05:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.