To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific prediction. Our approach is based on a novel technique to defend against adversarial evidence (i.e. faulty evidence due to artefacts) by filtering gradients during optimization. The defense does not depend on human-tuned parameters. It enables explanations which are both fine-grained and preserve the characteristics of images, such as edges and colors. The explanations are interpretable, suited for visualizing detailed evidence and can be tested as they are valid model inputs. We qualitatively and quantitatively evaluate our approach on a multitude of models and datasets.
Transitivity Recovering Decompositions: Interpretable and Robust Fine-Grained Relationships
University of Exeter（埃克塞特大学）；University of Trento（特伦托大学）；University of Tübingen（蒂宾根大学）；MPI for Informatics（马克斯·普朗克信息学研究所）；The Alan Turing Institute（艾伦·图灵研究所）；University of Surrey（萨里大学）
Deep learning models have achieved remarkable success in different areas of machine learning over the past decade; however, the size and complexity of these models make them difficult to understand. In an effort to make them more interpretable, several recent works focus on explaining parts of a deep neural network through human-interpretable, semantic attributes. However, it may be impossible to completely explain complex models using only semantic attributes. In this work, we propose to augment these attributes with a small set of uninterpretable features. Specifically, we develop a novel ex…
We present an interpretable deep model for fine-grained visual recognition. At the core of our method lies the integration of region-based part discovery and attribution within a deep neural network. Our model is trained using image-level object labels, and provides an interpretation of its results via the segmentation of object parts and the identification of their contributions towards classification. To facilitate the learning of object parts without direct supervision, we explore a simple prior of the occurrence of object parts. We demonstrate that this prior, when combined with our region…
Beihang University（北京航空航天大学）；Shenzhen Academy of Aerospace Technology（深圳航天技术研究院）
Fine-grained visual classification (FGVC) is challenging but more critical than traditional classification tasks. It requires distinguishing different subcategories with the inherently subtle intra-class object variations. Previous works focus on enhancing the feature representation ability using multiple granularities and discriminative regions based on the attention strategy or bounding boxes. However, these methods highly rely on deep neural networks which lack interpretability. We propose an Interpretable Attention Guided Network (IAGN) for fine-grained visual classification. The contribut…
SIC: Similarity-Based Interpretable Image Classification with Neural Networks
Lab for Artificial Intelligence in Medical Imaging, Technical University of Munich, Germany（医学影像中人工智能实验室，慕尼黑技术大学，德国）；Munich Center for Machine Learning (MCML), Germany（慕尼黑机器学习中心（MCML），德国）
University of Twente（特文特大学）；University of Duisburg-Essen（杜伊斯堡-埃森大学）
Prototype-based methods use interpretable representations to address the black-box nature of deep learning models, in contrast to post-hoc explanation methods that only approximate such models. We propose the Neural Prototype Tree (ProtoTree), an intrinsically interpretable deep learning method for fine-grained image recognition. ProtoTree combines prototype learning with decision trees, and thus results in a globally interpretable model by design. Additionally, ProtoTree can locally explain a single prediction by outlining a decision path through the tree. Each node in our binary tree contain…
Princeton University（普林斯顿大学）；The Chinese University of Hong Kong（香港中文大学）；Microsoft（微软）；UCLA（加州大学洛杉矶分校）；Beth Israel Deaconess Medical Center（贝斯以色列女执事医疗中心）；Microsoft Research New England（微软新英格兰研究院）；VSCO
We propose DeepMiner, a framework to discover interpretable representations in deep neural networks and to build explanations for medical predictions. By probing convolutional neural networks (CNNs) trained to classify cancer in mammograms, we show that many individual units in the final convolutional layer of a CNN respond strongly to diseased tissue concepts specified by the BI-RADS lexicon. After expert annotation of the interpretable units, our proposed method is able to generate explanations for CNN mammogram classification that are consistent with ground truth radiology reports on the Di…
Technical University of Munich（慕尼黑工业大学）；LMU University Hospital（慕尼黑大学医院）；Kyung Hee University（庆熙大学）
One principal approach for illuminating a black-box neural network is feature attribution, i.e. identifying the importance of input features for the network's prediction. The predictive information of features is recently proposed as a proxy for the measure of their importance. So far, the predictive information is only identified for latent features by placing an information bottleneck within the network. We propose a method to identify features with predictive information in the input domain. The method results in fine-grained identification of input features' information and is agnostic to …
The semantically disentangled latent subspace in GAN provides rich interpretable controls in image generation. This paper includes two contributions on semantic latent subspace analysis in the scenario of face generation using StyleGAN2. First, we propose a novel approach to disentangle latent subspace semantics by exploiting existing face analysis models, e.g., face parsers and face landmark detectors. These models provide the flexibility to construct various criterions with very concrete and interpretable semantic meanings (e.g., change face shape or change skin color) to restrict latent sub…
Beijing Key Laboratory of Security and Privacy in Intelligent Transportation, Beijing Jiaotong University（北京交通大学智能交通信息安全与隐私保护北京市重点实验室）
Deep learning (DL) models for image-based malware detection have exhibited their capability in producing high prediction accuracy. But model interpretability is posing challenges to their widespread application in security and safety-critical application domains. This paper aims for designing an Interpretable Ensemble learning approach for image-based Malware Detection (IEMD). We first propose a Selective Deep Ensemble Learning-based (SDEL) detector and then design an Ensemble Deep Taylor Decomposition (EDTD) approach, which can give the pixel-level explanation to SDEL detector outputs. Furthe…
Attri-Net: A Globally and Locally Inherently Interpretable Model for Multi-Label Classification Using Class-Specific Counterfactuals
Cluster of Excellence: Machine Learning - New Perspectives for Science, University of Tübingen（机器学习新视角科学卓越中心，图宾根大学）；Department of Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg（计算机科学系，埃尔兰根-纽伦堡弗里德里希-亚历山大大学）；Hertie Institute for Artificial Intelligence in Brain Health, University of Tübingen（脑健康人工智能研究所，图宾根大学）；Department of Diabetes, Endocrinology, Nutritional Medicine and Metabolism, Inselspital, Bern University Hospital, University of Bern（糖尿病、内分泌学、营养医学和代谢学系，伯恩大学医院，伯恩大学）；Faculty of Health Sciences and Medicine, University of Lucerne（健康科学与医学学院，卢塞恩大学）
Central Washington University（中央华盛顿大学）；University of Washington Tacoma（华盛顿大学塔科马分校）；Kensci Inc.（Kensci公司）
This paper surveys visual methods of explainability of Machine Learning (ML) with focus on moving from quasi-explanations that dominate in ML to domain-specific explanation supported by granular visuals. ML interpretation is fundamentally a human activity and visual methods are more readily interpretable. While efficient visual representations of high-dimensional data exist, the loss of interpretable information, occlusion, and clutter continue to be a challenge, which lead to quasi-explanations. We start with the motivation and the different definitions of explainability. The paper focuses on…
As machine learning algorithms are increasingly applied to high impact yet high risk tasks, such as medical diagnosis or autonomous driving, it is critical that researchers can explain how such algorithms arrived at their predictions. In recent years, a number of image saliency methods have been developed to summarize where highly complex neural networks "look" in an image for evidence for their predictions. However, these techniques are limited by their heuristic nature and architectural constraints. In this paper, we make two main contributions: First, we propose a general framework for lear…
A main issue preventing the use of Convolutional Neural Networks (CNN) in end user applications is the low level of transparency in the decision process. Previous work on CNN interpretability has mostly focused either on localizing the regions of the image that contribute to the result or on building an external model that generates plausible explanations. However, the former does not provide any semantic information and the latter does not guarantee the faithfulness of the explanation. We propose an intermediate representation composed of multiple Semantically Interpretable Activation Maps (S…
We present a novel multimodal interpretable VQA model that can answer the question more accurately and generate diverse explanations. Although researchers have proposed several methods that can generate human-readable and fine-grained natural language sentences to explain a model's decision, these methods have focused solely on the information in the image. Ideally, the model should refer to various information inside and outside the image to correctly generate explanations, just as we use background knowledge daily. The proposed method incorporates information from outside knowledge and multi…
Unsupervised Interpretable Basis Extraction for Concept-Based Visual Explanations
Information Technologies Institute, Centre for Research and Technology HELLAS（信息科技研究所，希腊研究中心与技术HELLOS）；Department of Advanced Computing Sciences, University of Maastricht（高级计算科学系，马斯特里赫特大学）
We study the problem of computer-assisted teaching with explanations. Conventional approaches for machine teaching typically only provide feedback at the instance level e.g., the category or label of the instance. However, it is intuitive that clear explanations from a knowledgeable teacher can significantly improve a student's ability to learn a new concept. To address these existing limitations, we propose a teaching framework that provides interpretable explanations as feedback and models how the learner incorporates this additional information. In the case of images, we show that we can au…
Unveiling Perceptual Artifacts: A Fine-Grained Benchmark for Interpretable AI-Generated Image Detection
Sun Yat-sen University（中山大学）；Peng Cheng Laboratory（鹏城实验室）；Xi’an Jiaotong University（西安交通大学）；Australian National University（澳大利亚国立大学）；Tsinghua University（清华大学）
Proto-FG3D: Prototype-based Interpretable Fine-Grained 3D Shape Classification
School of Information Science and Engineering（信息科学与工程学院）；University of Jinan（济南大学）；School of Control Science and Engineering（控制科学与工程学院）；Shandong University（山东大学）；Department of Computing and Decision Sciences（计算与决策科学系）；Lingnan University（岭南大学）；College of Computer Science（计算机科学学院）；Nankai University（南开大学）
Despite the growing discriminative capabilities of modern deep learning methods for recognition tasks, the inner workings of the state-of-art models still remain mostly black-boxes. In this paper, we propose a systematic interpretation of model parameters and hidden representations of Residual Temporal Convolutional Networks (Res-TCN) for action recognition in time-series data. We also propose a Feature Map Decoder as part of the interpretation analysis, which outputs a representation of model's hidden variables in the same domain as the input. Such analysis empowers us to expose model's chara…
Interpretable Single-View 3D Gaussian Splatting using Unsupervised Hierarchical Disentangled Representation Learning
Shanghai Jiao Tong University（上海交通大学）；Ningbo Institute of Digital Twin, Eastern Institute of Technology（东方理工大学宁波数字孪生研究院）；Hong Kong Polytechnic University（香港理工大学）
Freie Universität Berlin（柏林自由大学）；Weizenbaum Institut Berlin（柏林魏森鲍姆研究所）；TU Berlin（柏林工业大学）
A variety of methods exist to explain image classification models. However, whether they provide any benefit to users over simply comparing various inputs and the model's respective predictions remains unclear. We conducted a user study (N=240) to test how such a baseline explanation technique performs against concept-based and counterfactual explanations. To this end, we contribute a synthetic dataset generator capable of biasing individual attributes and quantifying their relevance to the model. In a study, we assess if participants can identify the relevant set of attributes compared to the…
Methodological Explainability Evaluation of an Interpretable Deep Learning Model for Post-Hepatectomy Liver Failure Prediction Incorporating Counterfactual Explanations and Layerwise Relevance Propagation: A Prospective In Silico Trial
Maastricht University（马斯特里赫特大学）；Maastricht University Medical Center+（马斯特里赫特大学医学中心+）；The First Affiliated Hospital of Sun Yat-sen University（中山大学附属第一医院）；Guizhou University（贵州大学）；Université Libre de Bruxelles（布鲁塞尔自由大学）
ConceptHash: Interpretable Fine-Grained Hashing via Concept Discovery
University of Surrey（萨里大学）；iFlyTek-Surrey Joint Research Centre on Artificial Intelligence（讯飞-萨里人工智能联合研究中心）；Surrey Institute for People-Centred Artificial Intelligence（萨里以人为本人工智能研究所）；CVSSP（视觉、语音和信号处理中心）
Eval3D: Interpretable and Fine-grained Evaluation for 3D Generation
Massachusetts Institute of Technology（麻省理工学院）；University of Washington（华盛顿大学）；New York University（纽约大学）；Wayve（Wayve公司）；Allen Institute for AI（艾伦人工智能研究所）；Cornell University（康奈尔大学）
Vision Language Model for Interpretable and Fine-grained Detection of Safety Compliance in Diverse Workplaces
School of Mechanical, Aerospace, and Manufacturing Engineering（机械、航空航天与制造工程学院）；University of Connecticut（康涅狄格大学）；Department of Computer Science（计算机科学系）；University of California Irvine（加州大学欧文分校）
University of Munich（慕尼黑大学）；Siemens AG, Corporate Technology（西门子股份公司，企业技术部）
Capsule Networks, as alternatives to Convolutional Neural Networks, have been proposed to recognize objects from images. The current literature demonstrates many advantages of CapsNets over CNNs. However, how to create explanations for individual classifications of CapsNets has not been well explored. The widely used saliency methods are mainly proposed for explaining CNN-based classifications; they create saliency map explanations by combining activation values and the corresponding gradients, e.g., Grad-CAM. These saliency methods require a specific architecture of the underlying classifiers…
A Simple Interpretable Transformer for Fine-Grained Image Classification and Analysis
The Ohio State University（俄亥俄州立大学）；Amazon Alexa（亚马逊 Alexa）；Virginia Tech（弗吉尼亚理工大学）；Princeton University（普林斯顿大学）；Rensselaer Polytechnic Institute（伦斯勒理工学院）
Beyond Heatmaps: Unsupervised Concept-Graph Reasoning for Interpretable Visual Explanation
German Research Center for Artificial Intelligence (DFKI)（德国人工智能研究中心）；Saarland University（萨尔大学）；Oldenburg University（奥尔登堡大学）；BEGO GmbH & Co. KG（BEGO有限两合公司）
The ability for a human to understand an Artificial Intelligence (AI) model's decision-making process is critical in enabling stakeholders to visualize model behavior, perform model debugging, promote trust in AI models, and assist in collaborative human-AI decision-making. As a result, the research fields of interpretable and explainable AI have gained traction within AI communities as well as interdisciplinary scientists seeking to apply AI in their subject areas. In this paper, we focus on advancing the state-of-the-art in interpreting multimodal models - a class of machine learning methods…
