Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks

• URL: http://arxiv.org/abs/2212.05946v3
• Date: Wed, 25 Oct 2023 12:27:47 GMT
• Title: Evaluation and Improvement of Interpretability for Self-Explainable Part-Prototype Networks
• Authors: Qihan Huang, Mengqi Xue, Wenqi Huang, Haofei Zhang, Jie Song, Yongcheng Jing, Mingli Song
• Abstract summary: Part-prototype networks have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts. We make the first attempt to quantitatively and objectively evaluate the interpretability of the part-prototype networks. We propose an elaborated part-prototype network with a shallow-deep feature alignment module and a score aggregation module to improve the interpretability of prototypes.
• Score: 43.821442711496154
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Part-prototype networks (e.g., ProtoPNet, ProtoTree, and ProtoPool) have attracted broad research interest for their intrinsic interpretability and comparable accuracy to non-interpretable counterparts. However, recent works find that the interpretability from prototypes is fragile, due to the semantic gap between the similarities in the feature space and that in the input space. In this work, we strive to address this challenge by making the first attempt to quantitatively and objectively evaluate the interpretability of the part-prototype networks. Specifically, we propose two evaluation metrics, termed as consistency score and stability score, to evaluate the explanation consistency across images and the explanation robustness against perturbations, respectively, both of which are essential for explanations taken into practice. Furthermore, we propose an elaborated part-prototype network with a shallow-deep feature alignment (SDFA) module and a score aggregation (SA) module to improve the interpretability of prototypes. We conduct systematical evaluation experiments and provide substantial discussions to uncover the interpretability of existing part-prototype networks. Experiments on three benchmarks across nine architectures demonstrate that our model achieves significantly superior performance to the state of the art, in both the accuracy and interpretability. Our code is available at https://github.com/hqhQAQ/EvalProtoPNet.

Related papers

• Enhanced Prototypical Part Network (EPPNet) For Explainable Image Classification Via Prototypes [16.528373143163275]
  We introduce the Enhanced Prototypical Part Network (EPPNet) for image classification. EPPNet achieves strong performance while discovering relevant prototypes that can be used to explain the classification results. Our evaluations on the CUB-200-2011 dataset show that the EPPNet outperforms state-of-the-art xAI-based methods.
  arXiv  Detail & Related papers  (2024-08-08T17:26:56Z)
• Improving Network Interpretability via Explanation Consistency Evaluation [56.14036428778861]
  We propose a framework that acquires more explainable activation heatmaps and simultaneously increase the model performance. Specifically, our framework introduces a new metric, i.e., explanation consistency, to reweight the training samples adaptively in model learning. Our framework then promotes the model learning by paying closer attention to those training samples with a high difference in explanations.
  arXiv  Detail & Related papers  (2024-08-08T17:20:08Z)
• Unifying Feature and Cost Aggregation with Transformers for Semantic and Visual Correspondence [51.54175067684008]
  This paper introduces a Transformer-based integrative feature and cost aggregation network designed for dense matching tasks. We first show that feature aggregation and cost aggregation exhibit distinct characteristics and reveal the potential for substantial benefits stemming from the judicious use of both aggregation processes. Our framework is evaluated on standard benchmarks for semantic matching, and also applied to geometric matching, where we show that our approach achieves significant improvements compared to existing methods.
  arXiv  Detail & Related papers  (2024-03-17T07:02:55Z)
• InterpretCC: Intrinsic User-Centric Interpretability through Global Mixture of Experts [31.738009841932374]
  Interpretability for neural networks is a trade-off between three key requirements. We present InterpretCC, a family of interpretable-by-design neural networks that guarantee human-centric interpretability.
  arXiv  Detail & Related papers  (2024-02-05T11:55:50Z)
• Decom--CAM: Tell Me What You See, In Details! Feature-Level Interpretation via Decomposition Class Activation Map [23.71680014689873]
  Class Activation Map (CAM) is widely used to interpret deep model predictions by highlighting object location. This paper proposes a new two-stage interpretability method called the Decomposition Class Activation Map (Decom-CAM) Our experiments demonstrate that the proposed Decom-CAM outperforms current state-of-the-art methods significantly.
  arXiv  Detail & Related papers  (2023-05-27T14:33:01Z)
• A Fine-grained Interpretability Evaluation Benchmark for Neural NLP [44.08113828762984]
  This benchmark covers three representative NLP tasks: sentiment analysis, textual similarity and reading comprehension. We provide token-level rationales that are carefully annotated to be sufficient, compact and comprehensive. We conduct experiments on three typical models with three saliency methods, and unveil their strengths and weakness in terms of interpretability.
  arXiv  Detail & Related papers  (2022-05-23T07:37:04Z)
• Attentional Prototype Inference for Few-Shot Segmentation [128.45753577331422]
  We propose attentional prototype inference (API), a probabilistic latent variable framework for few-shot segmentation. We define a global latent variable to represent the prototype of each object category, which we model as a probabilistic distribution. We conduct extensive experiments on four benchmarks, where our proposal obtains at least competitive and often better performance than state-of-the-art prototype-based methods.
  arXiv  Detail & Related papers  (2021-05-14T06:58:44Z)
• Toward Scalable and Unified Example-based Explanation and Outlier Detection [128.23117182137418]
  We argue for a broader adoption of prototype-based student networks capable of providing an example-based explanation for their prediction. We show that our prototype-based networks beyond similarity kernels deliver meaningful explanations and promising outlier detection results without compromising classification accuracy.
  arXiv  Detail & Related papers  (2020-11-11T05:58:17Z)
• P2ExNet: Patch-based Prototype Explanation Network [5.557646286040063]
  We propose a novel interpretable network scheme, designed to inherently use an explainable reasoning process inspired by the human cognition. P2ExNet reaches comparable performance when compared to its counterparts while inherently providing understandable and traceable decisions.
  arXiv  Detail & Related papers  (2020-05-05T08:45:43Z)
• Obtaining Faithful Interpretations from Compositional Neural Networks [72.41100663462191]
  We evaluate the intermediate outputs of NMNs on NLVR2 and DROP datasets. We find that the intermediate outputs differ from the expected output, illustrating that the network structure does not provide a faithful explanation of model behaviour.
  arXiv  Detail & Related papers  (2020-05-02T06:50:35Z)

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.