Less is More: Fewer Interpretable Region via Submodular Subset Selection

• URL: http://arxiv.org/abs/2402.09164v3
• Date: Thu, 5 Sep 2024 19:56:09 GMT
• Title: Less is More: Fewer Interpretable Region via Submodular Subset Selection
• Authors: Ruoyu Chen, Hua Zhang, Siyuan Liang, Jingzhi Li, Xiaochun Cao,
• Abstract summary: This paper re-models the above image attribution problem as a submodular subset selection problem. We construct a novel submodular function to discover more accurate small interpretation regions. For correctly predicted samples, the proposed method improves the Deletion and Insertion scores with an average of 4.9% and 2.5% gain relative to HSIC-Attribution.
• Score: 54.07758302264416
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Image attribution algorithms aim to identify important regions that are highly relevant to model decisions. Although existing attribution solutions can effectively assign importance to target elements, they still face the following challenges: 1) existing attribution methods generate inaccurate small regions thus misleading the direction of correct attribution, and 2) the model cannot produce good attribution results for samples with wrong predictions. To address the above challenges, this paper re-models the above image attribution problem as a submodular subset selection problem, aiming to enhance model interpretability using fewer regions. To address the lack of attention to local regions, we construct a novel submodular function to discover more accurate small interpretation regions. To enhance the attribution effect for all samples, we also impose four different constraints on the selection of sub-regions, i.e., confidence, effectiveness, consistency, and collaboration scores, to assess the importance of various subsets. Moreover, our theoretical analysis substantiates that the proposed function is in fact submodular. Extensive experiments show that the proposed method outperforms SOTA methods on two face datasets (Celeb-A and VGG-Face2) and one fine-grained dataset (CUB-200-2011). For correctly predicted samples, the proposed method improves the Deletion and Insertion scores with an average of 4.9% and 2.5% gain relative to HSIC-Attribution. For incorrectly predicted samples, our method achieves gains of 81.0% and 18.4% compared to the HSIC-Attribution algorithm in the average highest confidence and Insertion score respectively. The code is released at https://github.com/RuoyuChen10/SMDL-Attribution.

Related papers

• Improving Distribution Alignment with Diversity-based Sampling [0.0]
  Domain shifts are ubiquitous in machine learning, and can substantially degrade a model's performance when deployed to real-world data. This paper proposes to improve these estimates by inducing diversity in each sampled minibatch. It simultaneously balances the data and reduces the variance of the gradients, thereby enhancing the model's generalisation ability.
  arXiv  Detail & Related papers  (2024-10-05T17:26:03Z)
• Anomaly Detection Under Uncertainty Using Distributionally Robust Optimization Approach [0.9217021281095907]
  Anomaly detection is defined as the problem of finding data points that do not follow the patterns of the majority. The one-class Support Vector Machines (SVM) method aims to find a decision boundary to distinguish between normal data points and anomalies. A distributionally robust chance-constrained model is proposed in which the probability of misclassification is low.
  arXiv  Detail & Related papers  (2023-12-03T06:13:22Z)
• Distributional Shift Adaptation using Domain-Specific Features [41.91388601229745]
  In open-world scenarios, streaming big data can be Out-Of-Distribution (OOD) We propose a simple yet effective approach that relies on correlations in general regardless of whether the features are invariant or not. Our approach uses the most confidently predicted samples identified by an OOD base model to train a new model that effectively adapts to the target domain.
  arXiv  Detail & Related papers  (2022-11-09T04:16:21Z)
• Domain-Specific Risk Minimization for Out-of-Distribution Generalization [104.17683265084757]
  We first establish a generalization bound that explicitly considers the adaptivity gap. We propose effective gap estimation methods for guiding the selection of a better hypothesis for the target. The other method is minimizing the gap directly by adapting model parameters using online target samples.
  arXiv  Detail & Related papers  (2022-08-18T06:42:49Z)
• Fake It Till You Make It: Near-Distribution Novelty Detection by Score-Based Generative Models [54.182955830194445]
  existing models either fail or face a dramatic drop under the so-called near-distribution" setting. We propose to exploit a score-based generative model to produce synthetic near-distribution anomalous data. Our method improves the near-distribution novelty detection by 6% and passes the state-of-the-art by 1% to 5% across nine novelty detection benchmarks.
  arXiv  Detail & Related papers  (2022-05-28T02:02:53Z)
• Selecting Treatment Effects Models for Domain Adaptation Using Causal Knowledge [82.5462771088607]
  We propose a novel model selection metric specifically designed for ITE methods under the unsupervised domain adaptation setting. In particular, we propose selecting models whose predictions of interventions' effects satisfy known causal structures in the target domain.
  arXiv  Detail & Related papers  (2021-02-11T21:03:14Z)
• Learning Invariant Representations and Risks for Semi-supervised Domain Adaptation [109.73983088432364]
  We propose the first method that aims to simultaneously learn invariant representations and risks under the setting of semi-supervised domain adaptation (Semi-DA) We introduce the LIRR algorithm for jointly textbfLearning textbfInvariant textbfRepresentations and textbfRisks.
  arXiv  Detail & Related papers  (2020-10-09T15:42:35Z)
• Probabilistic Anchor Assignment with IoU Prediction for Object Detection [9.703212439661097]
  In object detection, determining which anchors to assign as positive or negative samples, known as anchor assignment, has been revealed as a core procedure that can significantly affect a model's performance. We propose a novel anchor assignment strategy that adaptively separates anchors into positive and negative samples for a ground truth bounding box according to the model's learning status.
  arXiv  Detail & Related papers  (2020-07-16T04:26:57Z)
• Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection [85.53263670166304]
  One-stage detector basically formulates object detection as dense classification and localization. Recent trend for one-stage detectors is to introduce an individual prediction branch to estimate the quality of localization. This paper delves into the representations of the above three fundamental elements: quality estimation, classification and localization.
  arXiv  Detail & Related papers  (2020-06-08T07:24:33Z)

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.