Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations

• URL: http://arxiv.org/abs/2401.14142v2
• Date: Mon, 26 Feb 2024 11:33:48 GMT
• Title: Energy-Based Concept Bottleneck Models: Unifying Prediction, Concept Intervention, and Probabilistic Interpretations
• Authors: Xinyue Xu, Yi Qin, Lu Mi, Hao Wang, Xiaomeng Li
• Abstract summary: Concept bottleneck models (CBMs) have been successful in providing concept-based interpretations for black-box deep learning models. We propose Energy-based Concept Bottleneck Models (ECBMs) Our ECBMs use a set of neural networks to define the joint energy of candidate (input, concept, class) quantifications.
• Score: 16.33960472610483
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Existing methods, such as concept bottleneck models (CBMs), have been successful in providing concept-based interpretations for black-box deep learning models. They typically work by predicting concepts given the input and then predicting the final class label given the predicted concepts. However, (1) they often fail to capture the high-order, nonlinear interaction between concepts, e.g., correcting a predicted concept (e.g., "yellow breast") does not help correct highly correlated concepts (e.g., "yellow belly"), leading to suboptimal final accuracy; (2) they cannot naturally quantify the complex conditional dependencies between different concepts and class labels (e.g., for an image with the class label "Kentucky Warbler" and a concept "black bill", what is the probability that the model correctly predicts another concept "black crown"), therefore failing to provide deeper insight into how a black-box model works. In response to these limitations, we propose Energy-based Concept Bottleneck Models (ECBMs). Our ECBMs use a set of neural networks to define the joint energy of candidate (input, concept, class) tuples. With such a unified interface, prediction, concept correction, and conditional dependency quantification are then represented as conditional probabilities, which are generated by composing different energy functions. Our ECBMs address both limitations of existing CBMs, providing higher accuracy and richer concept interpretations. Empirical results show that our approach outperforms the state-of-the-art on real-world datasets.

Related papers

• MulCPred: Learning Multi-modal Concepts for Explainable Pedestrian Action Prediction [57.483718822429346]
  MulCPred is proposed that explains its predictions based on multi-modal concepts represented by training samples. MulCPred is evaluated on multiple datasets and tasks.
  arXiv  Detail & Related papers  (2024-09-14T14:15:28Z)
• 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)
• Concept Bottleneck Models Without Predefined Concepts [26.156636891713745]
  We introduce an input-dependent concept selection mechanism that ensures only a small subset of concepts is used across all classes. We show that our approach improves downstream performance and narrows the performance gap to black-box models.
  arXiv  Detail & Related papers  (2024-07-04T13:34:50Z)
• Incremental Residual Concept Bottleneck Models [29.388549499546556]
  Concept Bottleneck Models (CBMs) map the black-box visual representations extracted by deep neural networks onto a set of interpretable concepts. We propose the Incremental Residual Concept Bottleneck Model (Res-CBM) to address the challenge of concept completeness. Our approach can be applied to any user-defined concept bank, as a post-hoc processing method to enhance the performance of any CBMs.
  arXiv  Detail & Related papers  (2024-04-13T12:02:19Z)
• Separable Multi-Concept Erasure from Diffusion Models [52.51972530398691]
  We propose a Separable Multi-concept Eraser (SepME) to eliminate unsafe concepts from large-scale diffusion models. The latter separates optimizable model weights, making each weight increment correspond to a specific concept erasure. Extensive experiments indicate the efficacy of our approach in eliminating concepts, preserving model performance, and offering flexibility in the erasure or recovery of various concepts.
  arXiv  Detail & Related papers  (2024-02-03T11:10:57Z)
• Implicit Concept Removal of Diffusion Models [92.55152501707995]
  Text-to-image (T2I) diffusion models often inadvertently generate unwanted concepts such as watermarks and unsafe images. We present the Geom-Erasing, a novel concept removal method based on the geometric-driven control.
  arXiv  Detail & Related papers  (2023-10-09T17:13:10Z)
• Probabilistic Concept Bottleneck Models [26.789507935869107]
  Interpretable models are designed to make decisions in a human-interpretable manner. In this study, we address the ambiguity issue that can harm reliability. We propose Probabilistic Concept Bottleneck Models (ProbCBM)
  arXiv  Detail & Related papers  (2023-06-02T14:38:58Z)
• Learn to explain yourself, when you can: Equipping Concept Bottleneck Models with the ability to abstain on their concept predictions [21.94901195358998]
  We show how to equip a neural network based classifier with the ability to abstain from predicting concepts when the concept labeling component is uncertain. Our model learns to provide rationales for its predictions, but only whenever it is sure the rationale is correct.
  arXiv  Detail & Related papers  (2022-11-21T18:07:14Z)
• Concept Gradient: Concept-based Interpretation Without Linear Assumption [77.96338722483226]
  Concept Activation Vector (CAV) relies on learning a linear relation between some latent representation of a given model and concepts. We proposed Concept Gradient (CG), extending concept-based interpretation beyond linear concept functions. We demonstrated CG outperforms CAV in both toy examples and real world datasets.
  arXiv  Detail & Related papers  (2022-08-31T17:06:46Z)
• ConceptDistil: Model-Agnostic Distillation of Concept Explanations [4.462334751640166]
  Concept-based explanations aims to fill the model interpretability gap for non-technical humans-in-the-loop. We propose ConceptDistil, a method to bring concept explanations to any black-box classifier using knowledge distillation. We validate ConceptDistil in a real world use-case, showing that it is able to optimize both tasks.
  arXiv  Detail & Related papers  (2022-05-07T08:58:54Z)
• Concept Bottleneck Models [79.91795150047804]
  State-of-the-art models today do not typically support the manipulation of concepts like "the existence of bone spurs" We revisit the classic idea of first predicting concepts that are provided at training time, and then using these concepts to predict the label. On x-ray grading and bird identification, concept bottleneck models achieve competitive accuracy with standard end-to-end models.
  arXiv  Detail & Related papers  (2020-07-09T07:47:28Z)

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.