Explaining the Black-box Smoothly- A Counterfactual Approach

v1v2v3 (latest)

Explaining the Black-box Smoothly- A Counterfactual Approach

11 January 2021

ArXiv (abs)PDF HTML

Papers citing "Explaining the Black-box Smoothly- A Counterfactual Approach"

6 / 56 papers shown

Title
U-Net: Convolutional Networks for Biomedical Image Segmentation Olaf Ronneberger Philipp Fischer Thomas Brox SSeg 3DV 1.9K 77,441 0 18 May 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 2.1K 150,364 0 22 Dec 2014
Object Detectors Emerge in Deep Scene CNNs Bolei Zhou A. Khosla Àgata Lapedriza A. Oliva Antonio Torralba ObjD 164 1,283 0 22 Dec 2014
Striving for Simplicity: The All Convolutional Net Jost Tobias Springenberg Alexey Dosovitskiy Thomas Brox Martin Riedmiller FAtt 254 4,681 0 21 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 1.7K 100,529 0 04 Sep 2014
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps Karen Simonyan Andrea Vedaldi Andrew Zisserman FAtt 317 7,321 0 20 Dec 2013