CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models

3 September 2021

Papers citing "CX-ToM: Counterfactual Explanations with Theory-of-Mind for Enhancing Human Trust in Image Recognition Models"

10 / 60 papers shown

Title
MovieQA: Understanding Stories in Movies through Question-Answering Makarand Tapaswi Yukun Zhu Rainer Stiefelhagen Antonio Torralba R. Urtasun Sanja Fidler 80 736 0 09 Dec 2015
Visualizing and Understanding Recurrent Networks A. Karpathy Justin Johnson Li Fei-Fei HAI 66 1,100 0 05 Jun 2015
Distilling the Knowledge in a Neural Network Geoffrey E. Hinton Oriol Vinyals J. Dean FedML 102 19,448 0 09 Mar 2015
The Bayesian Case Model: A Generative Approach for Case-Based Reasoning and Prototype Classification Been Kim Cynthia Rudin J. Shah 34 321 0 03 Mar 2015
Adam: A Method for Stochastic Optimization Diederik P. Kingma Jimmy Ba ODL 458 149,474 0 22 Dec 2014
Explaining and Harnessing Adversarial Examples Ian Goodfellow Jonathon Shlens Christian Szegedy AAML GAN 124 18,922 0 20 Dec 2014
Very Deep Convolutional Networks for Large-Scale Image Recognition Karen Simonyan Andrew Zisserman FAtt MDE 659 99,991 0 04 Sep 2014
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 848 39,383 0 01 Sep 2014
Playing Atari with Deep Reinforcement Learning Volodymyr Mnih Koray Kavukcuoglu David Silver Alex Graves Ioannis Antonoglou Daan Wierstra Martin Riedmiller 61 12,163 0 19 Dec 2013
Visualizing and Understanding Convolutional Networks Matthew D. Zeiler Rob Fergus FAtt SSL 169 15,825 0 12 Nov 2013