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Learning interpretable concept groups in cnns

Nettet16. jul. 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 … NettetAbstract: We propose a novel training methodology---Concept Group Learning (CGL)---that encourages training of interpretable CNN filters by partitioning filters in each layer …

[1710.00935] Interpretable Convolutional Neural Networks

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. 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 … can child support arrears be waived in ny https://mihperformance.com

Comparing the Interpretability of Deep Networks via Network …

NettetAbstract: We 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 trained to learn a single visual concept. We achieve this through a novel regularization strategy that forces filters in the same group … Nettet4. aug. 2024 · By applying the interpretability technique of linearly probing intermediate representations, we also demonstrate that interpretable pathology features such as nuclei density are learned by the proposed CNN architecture, confirming the increased transparency of this model. Nettet23. jun. 2024 · This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable CNN, in order to clarify knowledge … fishkill tire hours

Learning Interpretable Concept Groups in CNNs - NASA/ADS

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Learning interpretable concept groups in cnns

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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. Nettet16. jul. 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 …

Learning interpretable concept groups in cnns

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NettetWe propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into … Nettet16. jul. 2024 · share. Convolutional neural networks(CNNs) have been successfully used in a rangeof tasks. However, CNNs are often viewed as "black-box" and lack …

NettetWe propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into concept groups, each of which is trained to learn a single visual concept. We achieve this through a novel regularization strategy that forces filters in the same group to be … Nettet21. sep. 2024 · Abstract and Figures We propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by …

Nettet2. okt. 2024 · This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs. In an interpretable CNN, each filter in a high conv-layer represents a certain object part. We do not need any annotations of object parts or … NettetLearning Interpretable Concept Groups in CNNs Saurabh Varshneya1, Antoine Ledent , Robert A. Vandermeulen2, Yunwen Lei3, Matthias Enders4, Damian Borth5and Marius …

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.

NettetWe propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into … can child support be claimed on income taxNettet31. jul. 2024 · Convolutional neural networks (CNNs) have shown exceptional performance for a range of medical imaging tasks. However, conventional CNNs are not able to explain their reasoning process, therefore limiting their adoption in clinical practice. In this work, we propose an inherently interpretable CNN for regression using similarity-based … can child support arrears be reducedNettet9. mai 2024 · Our hypothesis was that the CNNs would utilize the information from the hand-motor cortex during a hand movement-related paradigm. To verify this hypothesis, we divided the recorded channels into two distinct, non-overlapping groups: (a) hand-motor channels and (b) non-motor channels. fishkill tire house