Learning interpretable concept groups in cnns
Nettet1. feb. 2024 · This paper presents a method to learn a decision tree to quantitatively explain the logic of each prediction of a pre-trained convolutional neural networks (CNNs). Our method boosts the following two aspects of network interpretability. 1) In the CNN, each filter in a high conv-layer must represent a specific object part, instead of … NettetWe propose a novel training methodology---Concept Group Learning (CGL)---that encourages training of interpretable CNN filters by partitioning filters in each layer into \emph{concept groups}, each of which is …
Learning interpretable concept groups in cnns
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Nettet31. mai 2024 · Saliency maps get a step further by providing an interpretable technique to investigate hidden layers in CNNs. A saliency map is a way to measure the spatial support of a particular class in each image. It is the oldest and most frequently used explanation method for interpreting the predictions of convolutional neural networks. Nettetpus of visual data. At the same time, this end-to-end learn-ing strategy hinders the explainability and interpretability of decisions made by CNNs. Recently, there has been an increasing number of works studying the inner workings of CNNs [38, 23, 22] and explaining the decisions made by these networks [42, 31, 39, 40]. Zhang et al. [41] …
Nettet6. apr. 2024 · Active learning facilitates faster algorithm training by proactively identifying high-value data points in unlabeled datasets . Consistent with SSL, active learning does not require many labeled instances and also focuses on existing unlabeled data. In active learning, the examples to be labeled are chosen carefully from large unlabeled data. NettetWe propose a novel training methodology-Concept Group Learning (CGL) ... Learning Interpretable Concept Groups in CNNs. Varshneya S; Ledent A; Vandermeulen R; et al. See more; IJCAI International Joint Conference on Artificial Intelligence (2024) 1061-1067. DOI: 10.24963/ijcai.2024/147.
NettetAbstract: We propose a novel training methodology---Concept Group Learning (CGL)---that encourages training of interpretable CNN filters by partitioning filters in each layer … NettetWe propose a novel training methodology -- Concept Group Learning (CGL) --that encourages training of interpretable CNN filters by partitioning filtersin each layer into …
Nettet31. des. 2024 · As a solution to this problem, explainable or interpretable machine learning (IML) models and methods for interpretation, respectively, have been proposed. Some classical machine learning models like decision trees or logistic regression models inherently allow for interpretation, at least when used for problems with a small number …
Nettet10. sep. 2024 · A framework called Network Dissection has been proposed to quantify the interpretability of any given CNN [ 1, 15 ]. Network dissection quantifies the interpretability of any given network by measuring the degree of alignment between the unit activation and the ground-truth labels in a pre-defined dictionary of concepts. t shirt printing woodbridge vaNettet21. sep. 2024 · Abstract and Figures We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by … philosophy\u0027s 16Nettet11. jun. 2024 · Extracting Interpretable Concept-Based Decision Trees from CNNs. Conner Chyung, Michael Tsang, Yan Liu. In an attempt to gather a deeper understanding of how convolutional neural networks (CNNs) reason about human-understandable concepts, we present a method to infer labeled concept data from hidden layer … t shirt printing worcester maNettet8. jan. 2024 · Interpretable CNNs for Object Classification. Quanshi Zhang, Xin Wang, Ying Nian Wu, Huilin Zhou, Song-Chun Zhu. This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN) for object classification, where each interpretable filter encodes features of a specific … philosophy\\u0027s 1dNettet1 1 institutetext: Princeton University, Princeton NJ 08544, USA 1 1 email: [email protected] ELUDE: Generating interpretable explanations via a decomposition into labelled and unlabelled features t shirt printing woodstock gaNettet3. nov. 2024 · Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as “black-box” and lack of … philosophy\u0027s 0oNettetWe propose a novel training methodology---Concept Group Learning (CGL)---that encourages training of interpretable CNN filters by partitioning filters in each layer into … philosophy\\u0027s 0g