Learning interpretable concept groups in cnns

Author: hnsd

August undefined, 2024

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. NettetWe propose a novel training methodology -- Concept Group Learning (CGL) -- that encourages training of interpretable CNN filters by partitioning filters in each layer into …

Learning Interpretable Concept Groups in CNNs

Nettet30. mar. 2024 · They found that interpretable CNNs usually encoded head patterns of animals in its top conv-layer for classification. Interpretable CNN has more consistent … 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 … mddfb bcc ffv

[1901.02413] Interpretable CNNs for Object Classification

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. Nettet8. jan. 2024 · This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN), where each interpretable filter encodes features of a specific object part. Our method does not require additional annotations of object parts or textures for supervision. 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 … md dermatics coupon

Learning Interpretable Microscopic Features of Tumor by Multi …

Sensors Special Issue : Explainable/Interpretable Machine Learning …

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 … NettetWe propose a novel training methodology -- Concept Group Learning (CGL) --that encourages training of interpretable CNN filters by partitioning filtersin each layer into … md dept. of transportationNettetLearning interpretable representations: A new trend in the scope of network interpretability is to learn inter- pretable feature representations in neural networks [15, 32, 21] in an un-/weakly-supervised manner. Capsule nets [27] and interpretable RCNN [37] learned interpretable features in intermediate layers. mdd f code severe

"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 … " - Learning interpretable concept groups in cnns

Learning interpretable concept groups in cnns

Nettet25. feb. 2024 · Convolutional neural networks (CNN) are known to learn an image representation that captures concepts relevant to the task, but do so in an implicit way that hampers model interpretability. However, one could argue that such a representation is hidden in the neurons and can be made explicit by teaching the model to recognize … Nettet1. nov. 2024 · This paper proposes a learning strategy that embeds object-part concepts into a pre-trained convolutional neural network (CNN), in an attempt to 1) explore …

Did you know?

NettetLearning Interpretable Pathology Features by Multi- ... 7 concept-based interpretability, ... (Wang et al., 2016). The training of CNNs for this task, however, presents multiple 25 challenges ... 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 …

NettetThis 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. Our method does not require additional annotations of object parts or textures for supervision. Instead, we use the … 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.

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 …

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.

NettetLearning Interpretable Concept Groups in CNNs Saurabh Varshneya1, Antoine Ledent , Robert A. Vandermeulen2, Yunwen Lei3, Matthias Enders4, Damian Borth5and Marius … md dept of veterans affairs cumberland mdNettet31. 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 … md design and build ctNettet3. 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 … md developmental agency llcNettetUniversity of Oklahoma. Jan 2024 - Present4 years 4 months. Norman, OK, USA. Constructed pipelines to curate, train, conduct experiments, and evaluate non-linear and linear machine learning ... md dept of veterans affairs baltimoreNettet30. mar. 2024 · Interpretable CNNs for Object Classification Abstract: This paper proposes a generic method to learn interpretable convolutional filters in a deep … md dermatology in indiaNettet10. 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. mdd first line treatmentNettet8. jan. 2024 · This paper proposes a generic method to learn interpretable convolutional filters in a deep convolutional neural network (CNN), where each interpretable filter … mdd geotechnical