Training Interpretable Convolutional Neural Networks by Differentiating
Class-specific Filters

Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters

16 July 2020

Hang Su

Jun Zhu

Papers citing "Training Interpretable Convolutional Neural Networks by Differentiating Class-specific Filters"

8 / 8 papers shown

Title
Disentangling Visual Transformers: Patch-level Interpretability for Image Classification Guillaume Jeanneret Loïc Simon F. Jurie ViT 44 0 0 24 Feb 2025
COMIX: Compositional Explanations using Prototypes S. Sivaprasad D. Kangin Plamen Angelov Mario Fritz 59 0 0 10 Jan 2025
Understanding the Role of Pathways in a Deep Neural Network Lei Lyu Chen Pang Jihua Wang 22 3 0 28 Feb 2024
PICNN: A Pathway towards Interpretable Convolutional Neural Networks Wengang Guo Jiayi Yang Huilin Yin Qijun Chen Wei Ye 18 3 0 19 Dec 2023
Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint Borui Zhang Wenzhao Zheng Jie Zhou Jiwen Lu AAML 23 7 0 18 Dec 2022
TCNL: Transparent and Controllable Network Learning Via Embedding Human-Guided Concepts Zhihao Wang Chuang Zhu 19 1 0 07 Oct 2022
Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes Jonathan Donnelly A. Barnett Chaofan Chen 3DH 17 127 0 29 Nov 2021
Do semantic parts emerge in Convolutional Neural Networks? Abel Gonzalez-Garcia Davide Modolo V. Ferrari 147 113 0 13 Jul 2016