Toward Simple and Robust Contrastive Explanations for Image Classification by Leveraging Instance Similarity and Concept Relevance

• URL: http://arxiv.org/abs/2506.23975v1
• Date: Mon, 30 Jun 2025 15:41:43 GMT
• Title: Toward Simple and Robust Contrastive Explanations for Image Classification by Leveraging Instance Similarity and Concept Relevance
• Authors: Yuliia Kaidashova, Bettina Finzel, Ute Schmid,
• Abstract summary: This work implements concept-based contrastive explanations for image classification by leveraging the similarity of instance embeddings and relevance of human-understandable concepts used by a fine-tuned deep learning model.<n>Our approach extracts concepts with their relevance score, computes contrasts for similar instances, and evaluates the resulting contrastive explanations based on explanation complexity.
• Score: 1.9897061813159418
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding why a classification model prefers one class over another for an input instance is the challenge of contrastive explanation. This work implements concept-based contrastive explanations for image classification by leveraging the similarity of instance embeddings and relevance of human-understandable concepts used by a fine-tuned deep learning model. Our approach extracts concepts with their relevance score, computes contrasts for similar instances, and evaluates the resulting contrastive explanations based on explanation complexity. Robustness is tested for different image augmentations. Two research questions are addressed: (1) whether explanation complexity varies across different relevance ranges, and (2) whether explanation complexity remains consistent under image augmentations such as rotation and noise. The results confirm that for our experiments higher concept relevance leads to shorter, less complex explanations, while lower relevance results in longer, more diffuse explanations. Additionally, explanations show varying degrees of robustness. The discussion of these findings offers insights into the potential of building more interpretable and robust AI systems.

Related papers

• CNN-based explanation ensembling for dataset, representation and explanations evaluation [1.1060425537315088]
  We explore the potential of ensembling explanations generated by deep classification models using convolutional model. Through experimentation and analysis, we aim to investigate the implications of combining explanations to uncover a more coherent and reliable patterns of the model's behavior.
  arXiv  Detail & Related papers  (2024-04-16T08:39:29Z)
• Interpretable Measures of Conceptual Similarity by Complexity-Constrained Descriptive Auto-Encoding [112.0878081944858]
  Quantifying the degree of similarity between images is a key copyright issue for image-based machine learning. We seek to define and compute a notion of "conceptual similarity" among images that captures high-level relations. Two highly dissimilar images can be discriminated early in their description, whereas conceptually dissimilar ones will need more detail to be distinguished.
  arXiv  Detail & Related papers  (2024-02-14T03:31:17Z)
• Contrastive Corpus Attribution for Explaining Representations [17.07084455770185]
  Most explanation methods explain a scalar model output. Recent works defined a scalar explanation output: a dot product-based similarity in the representation space to the sample being explained. We propose contrastive corpus similarity, a novel and semantically meaningful scalar explanation output.
  arXiv  Detail & Related papers  (2022-09-30T21:59:10Z)
• Understanding Contrastive Learning Requires Incorporating Inductive Biases [64.56006519908213]
  Recent attempts to theoretically explain the success of contrastive learning on downstream tasks prove guarantees depending on properties of em augmentations and the value of em contrastive loss of representations. We demonstrate that such analyses ignore em inductive biases of the function class and training algorithm, even em provably leading to vacuous guarantees in some settings.
  arXiv  Detail & Related papers  (2022-02-28T18:59:20Z)
• Contrastive Explanations for Model Interpretability [77.92370750072831]
  We propose a methodology to produce contrastive explanations for classification models. Our method is based on projecting model representation to a latent space. Our findings shed light on the ability of label-contrastive explanations to provide a more accurate and finer-grained interpretability of a model's decision.
  arXiv  Detail & Related papers  (2021-03-02T00:36:45Z)
• Explainers in the Wild: Making Surrogate Explainers Robust to Distortions through Perception [77.34726150561087]
  We propose a methodology to evaluate the effect of distortions in explanations by embedding perceptual distances. We generate explanations for images in the Imagenet-C dataset and demonstrate how using a perceptual distances in the surrogate explainer creates more coherent explanations for the distorted and reference images.
  arXiv  Detail & Related papers  (2021-02-22T12:38:53Z)
• This is not the Texture you are looking for! Introducing Novel Counterfactual Explanations for Non-Experts using Generative Adversarial Learning [59.17685450892182]
  counterfactual explanation systems try to enable a counterfactual reasoning by modifying the input image. We present a novel approach to generate such counterfactual image explanations based on adversarial image-to-image translation techniques. Our results show that our approach leads to significantly better results regarding mental models, explanation satisfaction, trust, emotions, and self-efficacy than two state-of-the art systems.
  arXiv  Detail & Related papers  (2020-12-22T10:08:05Z)
• Few-shot Visual Reasoning with Meta-analogical Contrastive Learning [141.2562447971]
  We propose to solve a few-shot (or low-shot) visual reasoning problem, by resorting to analogical reasoning. We extract structural relationships between elements in both domains, and enforce them to be as similar as possible with analogical learning. We validate our method on RAVEN dataset, on which it outperforms state-of-the-art method, with larger gains when the training data is scarce.
  arXiv  Detail & Related papers  (2020-07-23T14:00:34Z)
• Explainable Image Classification with Evidence Counterfactual [0.0]
  We introduce SEDC as a model-agnostic instance-level explanation method for image classification. For a given image, SEDC searches a small set of segments that, in case of removal, alters the classification. We compare SEDC(-T) with popular feature importance methods such as LRP, LIME and SHAP, and we describe how the mentioned importance ranking issues are addressed.
  arXiv  Detail & Related papers  (2020-04-16T08:02:48Z)

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.