Learning interpretable concept groups in cnns
Nettet31. 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 … Nettet11. apr. 2024 · Convolutional neural networks (CNNs) have achieved impressive results on imbalanced image data, but they still have difficulty generalizing to minority classes and their decisions are difficult to interpret. These problems are related because the method by which CNNs generalize to minority classes, which requires improvement, is wrapped in …
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 … 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 · Convolutional neural networks (CNNs) have been successfully used in a range of tasks. However, CNNs are often viewed as "black-box" and lack of … Nettet30. mar. 2024 · Interpretable CNNs for Object Classification Abstract: 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 object part.
NettetWe propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into … Nettet7. apr. 2024 · Nonetheless, three-round learning in 3D CNN provided comparable performance to those cutting-edge CNNs, demonstrating the effectiveness of the training procedure.
Nettet9. 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.
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. crowns frameNettet8. 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 … building secure computer systems uicNettet10. 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. crowns fortniteNettetWe 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 … building secure websitesNettet2. okt. 2024 · This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge … crowns for sale philippinesNettetWe 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 … building section vs longitudinal sectionNettetAbstract: 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 … crowns from china