Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations

• URL: http://arxiv.org/abs/2408.13438v3
• Date: Thu, 05 Jun 2025 23:54:01 GMT
• Title: Explainable Concept Generation through Vision-Language Preference Learning for Understanding Neural Networks' Internal Representations
• Authors: Aditya Taparia, Som Sagar, Ransalu Senanayake,
• Abstract summary: Concept-based methods have become a popular choice for explaining deep neural networks post-hoc.<n>We devise a reinforcement learning-based preference optimization algorithm that fine-tunes a vision-language generative model.<n>We demonstrate our method's ability to efficiently and reliably articulate diverse concepts.
• Score: 7.736445799116692
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Understanding the inner representation of a neural network helps users improve models. Concept-based methods have become a popular choice for explaining deep neural networks post-hoc because, unlike most other explainable AI techniques, they can be used to test high-level visual "concepts" that are not directly related to feature attributes. For instance, the concept of "stripes" is important to classify an image as a zebra. Concept-based explanation methods, however, require practitioners to guess and manually collect multiple candidate concept image sets, making the process labor-intensive and prone to overlooking important concepts. Addressing this limitation, in this paper, we frame concept image set creation as an image generation problem. However, since naively using a standard generative model does not result in meaningful concepts, we devise a reinforcement learning-based preference optimization (RLPO) algorithm that fine-tunes a vision-language generative model from approximate textual descriptions of concepts. Through a series of experiments, we demonstrate our method's ability to efficiently and reliably articulate diverse concepts that are otherwise challenging to craft manually.

Related papers

• Mod-Adapter: Tuning-Free and Versatile Multi-concept Personalization via Modulation Adapter [52.08332620725473]
  We propose a tuning-free method for multi-concept personalization that can effectively customize both object and abstract concepts without test-time fine-tuning.<n>Our method achieves state-of-the-art performance in multi-concept personalization, supported by quantitative, qualitative, and human evaluations.
  arXiv  Detail & Related papers  (2025-05-24T09:21:32Z)
• OmniPrism: Learning Disentangled Visual Concept for Image Generation [57.21097864811521]
  Creative visual concept generation often draws inspiration from specific concepts in a reference image to produce relevant outcomes. We propose OmniPrism, a visual concept disentangling approach for creative image generation. Our method learns disentangled concept representations guided by natural language and trains a diffusion model to incorporate these concepts.
  arXiv  Detail & Related papers  (2024-12-16T18:59:52Z)
• Restyling Unsupervised Concept Based Interpretable Networks with Generative Models [14.604305230535026]
  We propose a novel method that relies on mapping the concept features to the latent space of a pretrained generative model. We quantitatively ascertain the efficacy of our method in terms of accuracy of the interpretable prediction network, fidelity of reconstruction, as well as faithfulness and consistency of learnt concepts.
  arXiv  Detail & Related papers  (2024-07-01T14:39:41Z)
• LLM-assisted Concept Discovery: Automatically Identifying and Explaining Neuron Functions [15.381209058506078]
  Prior works have associated concepts with neurons based on examples of concepts or a pre-defined set of concepts. We propose to leverage multimodal large language models for automatic and open-ended concept discovery. We validate each concept by generating examples and counterexamples and evaluating the neuron's response on this new set of images.
  arXiv  Detail & Related papers  (2024-06-12T18:19:37Z)
• Visual Concept-driven Image Generation with Text-to-Image Diffusion Model [65.96212844602866]
  Text-to-image (TTI) models have demonstrated impressive results in generating high-resolution images of complex scenes. Recent approaches have extended these methods with personalization techniques that allow them to integrate user-illustrated concepts. However, the ability to generate images with multiple interacting concepts, such as human subjects, as well as concepts that may be entangled in one, or across multiple, image illustrations remains illusive. We propose a concept-driven TTI personalization framework that addresses these core challenges.
  arXiv  Detail & Related papers  (2024-02-18T07:28:37Z)
• Advancing Ante-Hoc Explainable Models through Generative Adversarial Networks [24.45212348373868]
  This paper presents a novel concept learning framework for enhancing model interpretability and performance in visual classification tasks. Our approach appends an unsupervised explanation generator to the primary classifier network and makes use of adversarial training. This work presents a significant step towards building inherently interpretable deep vision models with task-aligned concept representations.
  arXiv  Detail & Related papers  (2024-01-09T16:16:16Z)
• CEIR: Concept-based Explainable Image Representation Learning [0.4198865250277024]
  We introduce Concept-based Explainable Image Representation (CEIR) to derive high-quality representations without label dependency. Our method exhibits state-of-the-art unsupervised clustering performance on benchmarks such as CIFAR10, CIFAR100, and STL10. CEIR can seamlessly extract the related concept from open-world images without fine-tuning.
  arXiv  Detail & Related papers  (2023-12-17T15:37:41Z)
• CLiC: Concept Learning in Context [54.81654147248919]
  This paper builds upon recent advancements in visual concept learning. It involves acquiring a visual concept from a source image and subsequently applying it to an object in a target image. To localize the concept learning, we employ soft masks that contain both the concept within the mask and the surrounding image area.
  arXiv  Detail & Related papers  (2023-11-28T01:33:18Z)
• ConceptBed: Evaluating Concept Learning Abilities of Text-to-Image Diffusion Models [79.10890337599166]
  We introduce ConceptBed, a large-scale dataset that consists of 284 unique visual concepts and 33K composite text prompts. We evaluate visual concepts that are either objects, attributes, or styles, and also evaluate four dimensions of compositionality: counting, attributes, relations, and actions. Our results point to a trade-off between learning the concepts and preserving the compositionality which existing approaches struggle to overcome.
  arXiv  Detail & Related papers  (2023-06-07T18:00:38Z)
• Ablating Concepts in Text-to-Image Diffusion Models [57.9371041022838]
  Large-scale text-to-image diffusion models can generate high-fidelity images with powerful compositional ability. These models are typically trained on an enormous amount of Internet data, often containing copyrighted material, licensed images, and personal photos. We propose an efficient method of ablating concepts in the pretrained model, preventing the generation of a target concept.
  arXiv  Detail & Related papers  (2023-03-23T17:59:42Z)
• SegDiscover: Visual Concept Discovery via Unsupervised Semantic Segmentation [29.809900593362844]
  SegDiscover is a novel framework that discovers semantically meaningful visual concepts from imagery datasets with complex scenes without supervision. Our method generates concept primitives from raw images, discovering concepts by clustering in the latent space of a self-supervised pretrained encoder, and concept refinement via neural network smoothing.
  arXiv  Detail & Related papers  (2022-04-22T20:44:42Z)
• Human-Centered Concept Explanations for Neural Networks [47.71169918421306]
  We introduce concept explanations including the class of Concept Activation Vectors (CAV) We then discuss approaches to automatically extract concepts, and approaches to address some of their caveats. Finally, we discuss some case studies that showcase the utility of such concept-based explanations in synthetic settings and real world applications.
  arXiv  Detail & Related papers  (2022-02-25T01:27:31Z)
• Learning Representations by Predicting Bags of Visual Words [55.332200948110895]
  Self-supervised representation learning targets to learn convnet-based image representations from unlabeled data. Inspired by the success of NLP methods in this area, in this work we propose a self-supervised approach based on spatially dense image descriptions.
  arXiv  Detail & Related papers  (2020-02-27T16:45:25Z)

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.