Decontextualized learning for interpretable hierarchical representations of visual patterns
Max Planck Institute of Animal Behavior（马克斯·普朗克动物行为研究所）；University of Konstanz（康斯坦茨大学）；Centro de Investigaciones Científicas de las Huastecas Aguazarca, A.C.（华斯特卡斯阿瓜扎尔卡科学研究中心）；University of Würzburg（维尔茨堡大学）；Texas A&M University（得克萨斯农工大学）；Texas State University San Marcos（得克萨斯州立大学圣马科斯分校）
Apart from discriminative models for classification and object detection tasks, the application of deep convolutional neural networks to basic research utilizing natural imaging data has been somewhat limited; particularly in cases where a set of interpretable features for downstream analysis is needed, a key requirement for many scientific investigations. We present an algorithm and training paradigm designed specifically to address this: decontextualized hierarchical representation learning (DHRL). By combining a generative model chaining procedure with a ladder network architecture and latent space regularization for inference, DHRL address the limitations of small datasets and encourages a disentangled set of hierarchically organized features. In addition to providing a tractable path for analyzing complex hierarchal patterns using variation inference, this approach is generative and can be directly combined with empirical and theoretical approaches. To highlight the extensibility and usefulness of DHRL, we demonstrate this method in application to a question from evolutionary biology.
Supervised Deep Multimodal Matrix Factorization for Interpretable Brain Network Analysis
Dept. of Mathematics & Operational Research University of Mons（数学与运筹学系蒙斯大学）；Dept. of Computer Science & Engineering Lehigh University（计算机科学与工程系莱斯大学）；Dept. of Industrial & Systems Engineering Lehigh University（工业与系统工程系莱斯大学）
The recently developed variational autoencoders (VAEs) have proved to be an effective confluence of the rich representational power of neural networks with Bayesian methods. However, most work on VAEs use a rather simple prior over the latent variables such as standard normal distribution, thereby restricting its applications to relatively simple phenomena. In this work, we propose hierarchical nonparametric variational autoencoders, which combines tree-structured Bayesian nonparametric priors with VAEs, to enable infinite flexibility of the latent representation space. Both the neural paramet…
Hierarchical clustering is an effective and efficient approach widely used for classical image segmentation methods. However, many existing methods using neural networks generate segmentation masks directly from per-pixel features, complicating the architecture design and degrading the interpretability. In this work, we propose a simpler, more interpretable architecture, called HCFormer. HCFormer accomplishes image segmentation by bottom-up hierarchical clustering and allows us to interpret, visualize, and evaluate the intermediate results as hierarchical clustering results. HCFormer can addre…
A Hierarchical Sheaf Spectral Embedding Framework for Single-Cell RNA-seq Analysis
Department of Mathematics, Michigan State University（密歇根州立大学数学系）；Department of Biochemistry and Molecular Biology, Michigan State University（密歇根州立大学生物化学与分子生物学系）；Department of Electrical and Computer Engineering, Michigan State University（密歇根州立大学电气与计算机工程系）
University of Pittsburgh（匹兹堡大学）；University of Illinois at Chicago（伊利诺伊大学芝加哥分校）
Recently brain networks have been widely adopted to study brain dynamics, brain development and brain diseases. Graph representation learning techniques on brain functional networks can facilitate the discovery of novel biomarkers for clinical phenotypes and neurodegenerative diseases. However, current graph learning techniques have several issues on brain network mining. Firstly, most current graph learning models are designed for unsigned graph, which hinders the analysis of many signed network data (e.g., brain functional networks). Meanwhile, the insufficiency of brain network data limits …
Xidian University（西安电子科技大学）；The University of Texas at Austin（德克萨斯大学奥斯汀分校）
Embedded topic models are able to learn interpretable topics even with large and heavy-tailed vocabularies. However, they generally hold the Euclidean embedding space assumption, leading to a basic limitation in capturing hierarchical relations. To this end, we present a novel framework that introduces hyperbolic embeddings to represent words and topics. With the tree-likeness property of hyperbolic space, the underlying semantic hierarchy among words and topics can be better exploited to mine more interpretable topics. Furthermore, due to the superiority of hyperbolic geometry in representing…
Know-Center GmbH（Know-Center有限公司）；Materials Center Leoben Forschung GmbH（莱奥本材料研究中心有限公司）
In this paper, we frame homogeneous-feature multi-task learning (MTL) as a hierarchical representation learning problem, with one task-agnostic and multiple task-specific latent representations. Drawing inspiration from the information bottleneck principle and assuming an additive independent noise model between the task-agnostic and task-specific latent representations, we limit the information contained in each task-specific representation. It is shown that our resulting representations yield competitive performance for several MTL benchmarks. Furthermore, for certain setups, we show that th…
Vision models are interpretable when they classify objects on the basis of features that a person can directly understand. Recently, methods relying on visual feature prototypes have been developed for this purpose. However, in contrast to how humans categorize objects, these approaches have not yet made use of any taxonomical organization of class labels. With such an approach, for instance, we may see why a chimpanzee is classified as a chimpanzee, but not why it was considered to be a primate or even an animal. In this work we introduce a model that uses hierarchically organized prototypes …
Hierarchical Invariance for Robust and Interpretable Vision Tasks at Larger Scales
College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics（南京航空航天大学计算机科学与技术学院）；College of Cyber Security, Jinan University（暨南大学网络安全学院）；School of Cyber Science and Technology, Shenzhen Campus of Sun Yat-sen University（中山大学深圳校区网络空间安全学院）
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）
Hierarchical Vision Transformer with Prototypes for Interpretable Medical Image Classification
Ulm University Medical Center（乌尔姆大学医学中心）；BGZ - Bildgebungszentrum（BGZ影像中心）；i2SouI - Innovative Imaging in Surgical Oncology Ulm（乌尔姆外科肿瘤创新影像中心）；XAIRAD - Cooperation for Artificial Intelligence in Experimental Radiology（XAIRAD实验放射学人工智能合作中心）
Hierarchical structures are popular in recent vision transformers, however, they require sophisticated designs and massive datasets to work well. In this paper, we explore the idea of nesting basic local transformers on non-overlapping image blocks and aggregating them in a hierarchical way. We find that the block aggregation function plays a critical role in enabling cross-block non-local information communication. This observation leads us to design a simplified architecture that requires minor code changes upon the original vision transformer. The benefits of the proposed judiciously-select…
University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
While deep learning methods are increasingly being applied to tasks such as computer-aided diagnosis, these models are difficult to interpret, do not incorporate prior domain knowledge, and are often considered as a "black-box." The lack of model interpretability hinders them from being fully understood by target users such as radiologists. In this paper, we present a novel interpretable deep hierarchical semantic convolutional neural network (HSCNN) to predict whether a given pulmonary nodule observed on a computed tomography (CT) scan is malignant. Our network provides two levels of output: …
Hierarchical Salient Patch Identification for Interpretable Fundus Disease Localization
School of Electronic and Information Engineering, Tongji University（同济大学电子与信息工程学院）；Shanghai Eye Disease Prevention and Treatment Center（上海眼病防治中心）；School of Information Science and Technology, Fudan University（复旦大学信息科学与技术学院）
NTT Communication Science Laboratories, NTT Corporation（日本电信电话公司通信科学实验室）
This paper proposes the decision tree latent controller generative adversarial network (DTLC-GAN), an extension of a GAN that can learn hierarchically interpretable representations without relying on detailed supervision. To impose a hierarchical inclusion structure on latent variables, we incorporate a new architecture called the DTLC into the generator input. The DTLC has a multiple-layer tree structure in which the ON or OFF of the child node codes is controlled by the parent node codes. By using this architecture hierarchically, we can obtain the latent space in which the lower layer codes…
Harbin Engineering University（哈尔滨工程大学）；NC State University（北卡罗来纳州立大学）；University of California, Los Angeles（加利福尼亚大学洛杉矶分校）
This paper proposes to learn hierarchical compositional AND-OR model for interpretable image synthesis by sparsifying the generator network. The proposed method adopts the scene-objects-parts-subparts-primitives hierarchy in image representation. A scene has different types (i.e., OR) each of which consists of a number of objects (i.e., AND). This can be recursively formulated across the scene-objects-parts-subparts hierarchy and is terminated at the primitive level (e.g., wavelets-like basis). To realize this AND-OR hierarchy in image synthesis, we learn a generator network that consists of t…
Manchester Metropolitan University（曼彻斯特城市大学）；Aerospace Information Research Institute, Chinese Academy of Sciences（中国科学院空天信息创新研究院）；Brunel University（布鲁内尔大学）
Spectral-spatial based deep learning models have recently proven to be effective in hyperspectral image (HSI) classification for various earth monitoring applications such as land cover classification and agricultural monitoring. However, due to the nature of "black-box" model representation, how to explain and interpret the learning process and the model decision, especially for vegetation classification, remains an open challenge. This study proposes a novel interpretable deep learning model -- a biologically interpretable two-stage deep neural network (BIT-DNN), by incorporating the prior-k…
Compositional structures between parts and objects are inherent in natural scenes. Modeling such compositional hierarchies via unsupervised learning can bring various benefits such as interpretability and transferability, which are important in many downstream tasks. In this paper, we propose the first deep latent variable model, called RICH, for learning Representation of Interpretable Compositional Hierarchies. At the core of RICH is a latent scene graph representation that organizes the entities of a scene into a tree structure according to their compositional relationships. During inferenc…
SurfAge-Net: A Hierarchical Surface-Based Network for Interpretable Fine-Grained Brain Age Prediction
Gansu Provincial Key Laboratory of Wearable Computing, School of Information Science and Engineering, Lanzhou University（甘肃省可穿戴计算重点实验室，信息科学与工程学院，兰州大学）；Department of Medical Imaging Center, Gansu Provincial Maternity and Child-Care Hospital（医学影像中心，甘肃省妇幼保健院）；Department of Magnetic Resonance, Lanzhou University Second Hospital（磁共振科，兰州大学第二医院）；Department of Computer Science and Technology, Harbin Institute of Technology (Shenzhen)（计算机科学与技术系，哈尔滨工业大学（深圳））；Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University（教育部生物医学工程重点实验室，生物医学工程学院，浙江大学）
HICEScore: A Hierarchical Metric for Image Captioning Evaluation
Xidian University（西安电子科技大学）；National Key Laboratory of Radar Signal Processing（雷达信号处理国家重点实验室）；State Key Laboratory of Integrated Service Networks（综合业务网理论及关键技术国家重点实验室）
Bodhi VLM: Privacy-Alignment Modeling for Hierarchical Visual Representations in Vision Backbones and VLM Encoders via Bottom-Up and Top-Down Feature Search
Auckland University of Technology（奥克兰技术大学）；Resideo Technologies, Inc.（Resideo技术公司）；Guilin University of Electronic Technology（桂林电子科技大学）；Universidad de Chile（智利大学）
