Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networks

28 September 2022

Papers citing "Recipro-CAM: Fast gradient-free visual explanations for convolutional neural networks"

5 / 5 papers shown

Title
Top-GAP: Integrating Size Priors in CNNs for more Interpretability, Robustness, and Bias Mitigation Lars Nieradzik Henrike Stephani Janis Keuper FAtt AAML 36 0 0 07 Sep 2024
ViT-ReciproCAM: Gradient and Attention-Free Visual Explanations for Vision Transformer Seokhyun Byun Won-Jo Lee FAtt 18 4 0 04 Oct 2023
Revisiting The Evaluation of Class Activation Mapping for Explainability: A Novel Metric and Experimental Analysis Samuele Poppi Marcella Cornia Lorenzo Baraldi Rita Cucchiara FAtt 125 33 0 20 Apr 2021
Aggregated Residual Transformations for Deep Neural Networks Saining Xie Ross B. Girshick Piotr Dollár Z. Tu Kaiming He 297 10,216 0 16 Nov 2016
ImageNet Large Scale Visual Recognition Challenge Olga Russakovsky Jia Deng Hao Su J. Krause S. Satheesh ... A. Karpathy A. Khosla Michael S. Bernstein Alexander C. Berg Li Fei-Fei VLM ObjD 296 39,194 0 01 Sep 2014