Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency

• URL: http://arxiv.org/abs/2406.08840v1
• Date: Thu, 13 Jun 2024 06:04:34 GMT
• Title: Conceptual Learning via Embedding Approximations for Reinforcing Interpretability and Transparency
• Authors: Maor Dikter, Tsachi Blau, Chaim Baskin,
• Abstract summary: Concept bottleneck models (CBMs) have emerged as critical tools in domains where interpretability is paramount. This study proposes underlinetextbfConceptual underlinetextbfLbedding via underlinetextbfEmbedding underlinetextbfApproximations for underlinetextbfReinforcing Interpretability and Transparency.
• Score: 2.7719338074999547
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Concept bottleneck models (CBMs) have emerged as critical tools in domains where interpretability is paramount. These models rely on predefined textual descriptions, referred to as concepts, to inform their decision-making process and offer more accurate reasoning. As a result, the selection of concepts used in the model is of utmost significance. This study proposes \underline{\textbf{C}}onceptual \underline{\textbf{L}}earning via \underline{\textbf{E}}mbedding \underline{\textbf{A}}pproximations for \underline{\textbf{R}}einforcing Interpretability and Transparency, abbreviated as CLEAR, a framework for constructing a CBM for image classification. Using score matching and Langevin sampling, we approximate the embedding of concepts within the latent space of a vision-language model (VLM) by learning the scores associated with the joint distribution of images and concepts. A concept selection process is then employed to optimize the similarity between the learned embeddings and the predefined ones. The derived bottleneck offers insights into the CBM's decision-making process, enabling more comprehensive interpretations. Our approach was evaluated through extensive experiments and achieved state-of-the-art performance on various benchmarks. The code for our experiments is available at https://github.com/clearProject/CLEAR/tree/main

Related papers

• Explain via Any Concept: Concept Bottleneck Model with Open Vocabulary Concepts [8.028021897214238]
  "OpenCBM" is the first CBM with concepts of open vocabularies. Our model significantly outperforms the previous state-of-the-art CBM by 9% in the classification accuracy on the benchmark dataset CUB-200-2011.
  arXiv  Detail & Related papers  (2024-08-05T06:42:00Z)
• 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)
• MCPNet: An Interpretable Classifier via Multi-Level Concept Prototypes [24.28807025839685]
  We argue that explanations lacking insights into the decision processes of low and mid-level features are neither fully faithful nor useful. We propose a novel paradigm that learns and aligns multi-level concept prototype distributions for classification purposes via Class-aware Concept Distribution (CCD) loss.
  arXiv  Detail & Related papers  (2024-04-13T11:13:56Z)
• Sparse Concept Bottleneck Models: Gumbel Tricks in Contrastive Learning [86.15009879251386]
  We propose a novel architecture and method of explainable classification with Concept Bottleneck Models (CBM) CBMs require an additional set of concepts to leverage. We show a significant increase in accuracy using sparse hidden layers in CLIP-based bottleneck models.
  arXiv  Detail & Related papers  (2024-04-04T09:43:43Z)
• Beyond Concept Bottleneck Models: How to Make Black Boxes Intervenable? [8.391254800873599]
  We introduce a method to perform concept-based interventions on pretrained neural networks, which are not interpretable by design. We formalise the notion of intervenability as a measure of the effectiveness of concept-based interventions and leverage this definition to fine-tune black boxes.
  arXiv  Detail & Related papers  (2024-01-24T16:02:14Z)
• Rewrite Caption Semantics: Bridging Semantic Gaps for Language-Supervised Semantic Segmentation [100.81837601210597]
  We propose Concept Curation (CoCu) to bridge the gap between visual and textual semantics in pre-training data. CoCu achieves superb zero-shot transfer performance and greatly boosts language-supervised segmentation baseline by a large margin.
  arXiv  Detail & Related papers  (2023-09-24T00:05:39Z)
• Sparse Linear Concept Discovery Models [11.138948381367133]
  Concept Bottleneck Models (CBMs) constitute a popular approach where hidden layers are tied to human understandable concepts. We propose a simple yet highly intuitive interpretable framework based on Contrastive Language Image models and a single sparse linear layer. We experimentally show, our framework not only outperforms recent CBM approaches accuracy-wise, but it also yields high per example concept sparsity.
  arXiv  Detail & Related papers  (2023-08-21T15:16:19Z)
• Advancing Visual Grounding with Scene Knowledge: Benchmark and Method [74.72663425217522]
  Visual grounding (VG) aims to establish fine-grained alignment between vision and language. Most existing VG datasets are constructed using simple description texts. We propose a novel benchmark of underlineScene underlineKnowledge-guided underlineVisual underlineGrounding.
  arXiv  Detail & Related papers  (2023-07-21T13:06:02Z)
• ProbVLM: Probabilistic Adapter for Frozen Vision-Language Models [69.50316788263433]
  We propose ProbVLM, a probabilistic adapter that estimates probability distributions for the embeddings of pre-trained vision-language models. We quantify the calibration of embedding uncertainties in retrieval tasks and show that ProbVLM outperforms other methods. We present a novel technique for visualizing the embedding distributions using a large-scale pre-trained latent diffusion model.
  arXiv  Detail & Related papers  (2023-07-01T18:16:06Z)
• 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)
• GlanceNets: Interpretabile, Leak-proof Concept-based Models [23.7625973884849]
  Concept-based models (CBMs) combine high-performance and interpretability by acquiring and reasoning with a vocabulary of high-level concepts. We provide a clear definition of interpretability in terms of alignment between the model's representation and an underlying data generation process. We introduce GlanceNets, a new CBM that exploits techniques from disentangled representation learning and open-set recognition to achieve alignment.
  arXiv  Detail & Related papers  (2022-05-31T08:53:53Z)

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.