Learning interpretable concept groups in cnns

Author: igsb

August undefined, 2024

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

Learning Interpretable Concept Groups in CNNs - OpenHive

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 … 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 ... chemists in richmond nsw

Learning Interpretable Concept Groups in CNNs Papers With …

NettetWe propose a novel training methodology -- Concept Group Learning (CGL) --that encourages training of interpretable CNN filters by partitioning filtersin each layer into concept groups, each of which is trained to learn a singlevisual concept. We achieve this through a novel regularization strategy 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. 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 … flightline insignia

Interpretable CNNs for Object Classification - IEEE Xplore

Machine learning for synergistic network pharmacology: a …

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 … flight line interiorNettetpus 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] … flightline injury

"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. " - Learning interpretable concept groups in cnns

Learning interpretable concept groups in cnns

Nettet3. 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 … Nettet1 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

Did you know?

Nettet14. apr. 2024 · It was seen that deep learning approaches were used in forecasting studies in order to increase the quality of health services provided during the COVID-19 epidemic, to alleviate the workload of ... 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 …

Nettet8. 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 … 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 …

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

NettetTo ensure meaningfulness of the extracted concepts, we exclude the following two types of clus- ters: 1) Clusters that have segments that only coming from a single image or a very few number of images. 2) Clusters with segments less than # segments.

NettetLearning 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. chemists in rainham kentNettet23. jun. 2024 · This paper proposes a method to modify a traditional convolutional neural network (CNN) into an interpretable CNN, in order to clarify knowledge … chemists in ripley derbyshireNettet7. apr. 2024 · Nonetheless, three-round learning in 3D CNN provided comparable performance to those cutting-edge CNNs, demonstrating the effectiveness of the training procedure. chemists in rockingham waNettet16. 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 … flightline interiors wisconsinNettetLearning interpretable concept groups in CNNs. (2024). Proceedings of the 30th International Joint Conference on Artificial Intelligence, Montreal, 2024 August 19-27. … chemists in rochford essexNettetWe 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 … chemists in rotherham town centreNettetWe propose a novel training methodology---Concept Group Learning (CGL)---that encourages training of interpretable CNN filters by partitioning filters in each layer into … flightline interiors rv-12