With the increasing demands for accountability, interpretability is becoming an essential capability for real-world AI applications. However, most methods utilize post-hoc approaches rather than training the interpretable model. In this article, we propose a novel interpretable model based on the concept bottleneck model (CBM). CBM uses concept labels to train an intermediate layer as the additional visible layer. However, because the number of concept labels restricts the dimension of this layer, it is difficult to obtain high accuracy with a small number of labels. To address this issue, we integrate supervised concepts with unsupervised ones trained with self-explaining neural networks (SENNs). By seamlessly training these two types of concepts while reducing the amount of computation, we can obtain both supervised and unsupervised concepts simultaneously, even for large-sized images. We refer to the proposed model as the concept bottleneck model with additional unsupervised concepts (CBM-AUC). We experimentally confirmed that the proposed model outperformed CBM and SENN. We also visualized the saliency map of each concept and confirmed that it was consistent with the semantic meanings.
Visual concept discovery has long been deemed important to improve interpretability of neural networks, because a bank of semantically meaningful concepts would provide us with a starting point for building machine learning models that exhibit intelligible reasoning process. Previous methods have disadvantages: either they rely on labelled support sets that incorporate human biases for objects that are "useful," or they fail to identify multiple concepts that occur within a single image. We reframe the concept discovery task as an unsupervised semantic segmentation problem, and present SegDisc…
Sun Yat-sen University（中山大学）；SUNY at Buffalo（纽约州立大学布法罗分校）
Many unsupervised hashing methods are implicitly established on the idea of reconstructing the input data, which basically encourages the hashing codes to retain as much information of original data as possible. However, this requirement may force the models spending lots of their effort on reconstructing the unuseful background information, while ignoring to preserve the discriminative semantic information that is more important for the hashing task. To tackle this problem, inspired by the recent success of contrastive learning in learning continuous representations, we propose to adapt this …
Provable Responsible AI and Data Analytics (PRADA) Lab（可证明的负责任AI与数据分析（PRADA）实验室）；King Abdullah University of Science and Technology（阿卜杜拉国王科技大学）；Shanghai Jiao Tong University（上海交通大学）；The University of Auckland（奥克兰大学）；RIKEN Center for Advanced Intelligence Project (AIP)（理化学研究所先进智能项目中心（AIP））；Arizona State University（亚利桑那州立大学）；Stony Brook University（石溪大学）
We present a framework for learning disentangled representation of CapsNet by information bottleneck constraint that distills information into a compact form and motivates to learn an interpretable factorized capsule. In our $β$-CapsNet framework, hyperparameter $β$ is utilized to trade-off disentanglement and other tasks, variational inference is utilized to convert the information bottleneck term into a KL divergence that is approximated as a constraint on the mean of the capsule. For supervised learning, class independent mask vector is used for understanding the types of variations synthet…
University of Virginia（弗吉尼亚大学）；KTH Royal Institute of Technology（瑞典皇家理工学院）；University of Georgia（佐治亚大学）；Shanghai Jiao Tong University（上海交通大学）；HKUST（香港科技大学）；Ant Group（蚂蚁集团）
MIT CSAIL（麻省理工学院计算机科学与人工智能实验室）；University of Tübingen（图宾根大学）；Google Brain（谷歌大脑）
Humans are able to rapidly understand scenes by utilizing concepts extracted from prior experience. Such concepts are diverse, and include global scene descriptors, such as the weather or lighting, as well as local scene descriptors, such as the color or size of a particular object. So far, unsupervised discovery of concepts has focused on either modeling the global scene-level or the local object-level factors of variation, but not both. In this work, we propose COMET, which discovers and represents concepts as separate energy functions, enabling us to represent both global concepts as well a…
Concept Complement Bottleneck Model for Interpretable Medical Image Diagnosis
Hong Kong University of Science and Technology（香港科技大学）；Department of Computer Science and Engineering（计算机科学与工程系）；Department of Chemical and Biological Engineering（化学与生物工程系）；Division of Life Science（生命科学部）
University of California Los Angeles（加州大学洛杉矶分校）；University College London（伦敦大学学院）；USC Information Sciences Institute（南加州大学信息科学研究所）；Imperial College London（帝国理工学院）；U.S. Army Research Laboratory（美国陆军研究实验室）；U.S. Air Force Research Lab（美国空军研究实验室）
Recent efforts in interpretable deep learning models have shown that concept-based explanation methods achieve competitive accuracy with standard end-to-end models and enable reasoning and intervention about extracted high-level visual concepts from images, e.g., identifying the wing color and beak length for bird-species classification. However, these concept bottleneck models rely on a necessary and sufficient set of predefined concepts-which is intractable for complex tasks such as video classification. For complex tasks, the labels and the relationship between visual elements span many fra…
TRACE: A Concept Bottleneck Model for Longitudinal 3D Glioblastoma Response Assessment
Department of Biomedical and Healthcare Data Engineering, Faculty of Engineering, Cairo University（生物医学与健康数据工程系，工程学院，开罗大学）；German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心（DFKI））；University of Oldenburg（奥尔登堡大学）
Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
Unité d’Informatique et d’Ingénierie des Systèmes ENSTA Paris, Institut Polytechnique de Paris（ENSTA巴黎信息与系统工程单位，巴黎理工学院）；Department of Information Engineering and Computer Science University of Trento（信息工程与计算机科学系，特伦托大学）
Explainable fetal ultrasound quality assessment with progressive concept bottleneck models
Technical University of Denmark（丹麦技术大学）；Slagelse Hospital（斯莱格瑟医院）；CAMES；Copenhagen University Hospital Rigshospitalet（哥本哈根大学医院丽格医院）；University of Copenhagen（哥本哈根大学）
MVP-CBM:Multi-layer Visual Preference-enhanced Concept Bottleneck Model for Explainable Medical Image Classification
School of Biomedical Engineering, Division of Life Sciences and Medicine, University of Science and Technology of China（中国科学技术大学生物医学工程学院、生命科学与医学系）；Suzhou Institute for Advanced Research, University of Science and Technology of China（中国科学技术大学苏等候研院）；Jiangsu Provincial Key Laboratory of Multimodal Digital Twin Technology（江苏省多模态数字孪生技术省级重点实验室）；Medical Business Department, iFlytek Co., Ltd（iFlytek有限公司医疗业务部）；State Key Laboratory of Precision and Intelligent Chemistry, USTC（精准与智能化学国家重点实验室）
On the Faithfulness of Post-Hoc Concept Bottleneck Models
Institute of Data Science, German Aerospace Center（数据科学研究所，德国航空航天中心）；Computer Vision Group Jena, Friedrich Schiller University Jena（贾恩计算机视觉小组，弗里德里希·席勒大学贾恩）；GEOMAR Helmholtz Centre for Ocean Research Kiel（基尔海洋研究赫尔姆霍茨中心）
Attribute-formed Class-specific Concept Space: Endowing Language Bottleneck Model with Better Interpretability and Scalability
University of Electronic Science and Technology of China（电子科技大学）；Southwest Jiaotong University（西南交通大学）；University of Minnesota（明尼苏达大学）；Harvard University（哈佛大学）；Nanyang Technological University（南洋理工大学）
Univ. Lille（里尔大学）；CNRS（法国国家科学研究中心）；Centrale Lille（里尔中央理工学院）；IMT Lille Douai（IMT里尔杜埃矿业电信学院）
Spiking neural networks (SNNs) equipped with latency coding and spike-timing dependent plasticity rules offer an alternative to solve the data and energy bottlenecks of standard computer vision approaches: they can learn visual features without supervision and can be implemented by ultra-low power hardware architectures. However, their performance in image classification has never been evaluated on recent image datasets. In this paper, we compare SNNs to auto-encoders on three visual recognition datasets, and extend the use of SNNs to color images. The analysis of the results helps us identify…
