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Striving for Simplicity: The All Convolutional Net
International Conference on Learning Representations (ICLR), 2014
21 December 2014
Jost Tobias Springenberg
Alexey Dosovitskiy
Thomas Brox
Martin Riedmiller
FAtt
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Papers citing
"Striving for Simplicity: The All Convolutional Net"
50 / 1,917 papers shown
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217
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Quantitative Evaluations on Saliency Methods: An Experimental Study
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Differentiable Programming à la Moreau
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Stefan Kramer
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Peter Tiño
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Towards Robust Explanations for Deep Neural Networks
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M. Dyrba
Moritz Hanzig
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180
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FCFR-Net: Feature Fusion based Coarse-to-Fine Residual Learning for Depth Completion
AAAI Conference on Artificial Intelligence (AAAI), 2020
Lina Liu
Xibin Song
Xiaoyang Lyu
Junwei Diao
Mengmeng Wang
Yong Liu
Liangjun Zhang
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15 Dec 2020
Combining Similarity and Adversarial Learning to Generate Visual Explanation: Application to Medical Image Classification
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C´eline Hudelot
P. Cournède
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Woo-Jeoung Nam
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Representaciones del aprendizaje reutilizando los gradientes de la retropropagacion
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Deep Learning for Medical Anomaly Detection -- A Survey
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Tharindu Fernando
Harshala Gammulle
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Clinton Fookes
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04 Dec 2020
Self-Explaining Structures Improve NLP Models
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Chun Fan
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323
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03 Dec 2020
Interpretable Graph Capsule Networks for Object Recognition
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Jindong Gu
Volker Tresp
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263
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Rethinking Positive Aggregation and Propagation of Gradients in Gradient-based Saliency Methods
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Semi-Supervised Learning for Sparsely-Labeled Sequential Data: Application to Healthcare Video Processing
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A Study on the Uncertainty of Convolutional Layers in Deep Neural Networks
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Advancing diagnostic performance and clinical usability of neural networks via adversarial training and dual batch normalization
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T. Han
S. Nebelung
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Combining Semantic Guidance and Deep Reinforcement Learning For Generating Human Level Paintings
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CAFE-GAN: Arbitrary Face Attribute Editing with Complementary Attention Feature
European Conference on Computer Vision (ECCV), 2020
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Detecting hidden signs of diabetes in external eye photographs
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A. Mitani
I. Traynis
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Multiresolution Knowledge Distillation for Anomaly Detection
Computer Vision and Pattern Recognition (CVPR), 2020
Mohammadreza Salehi
Niousha Sadjadi
Soroosh Baselizadeh
M. Rohban
Hamid R. Rabiee
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Interpreting Super-Resolution Networks with Local Attribution Maps
Computer Vision and Pattern Recognition (CVPR), 2020
Jinjin Gu
Chao Dong
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Explaining by Removing: A Unified Framework for Model Explanation
Journal of machine learning research (JMLR), 2020
Ian Covert
Scott M. Lundberg
Su-In Lee
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Deep learning insights into cosmological structure formation
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140
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Use HiResCAM instead of Grad-CAM for faithful explanations of convolutional neural networks
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Deep Interpretable Classification and Weakly-Supervised Segmentation of Histology Images via Max-Min Uncertainty
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One Explanation is Not Enough: Structured Attention Graphs for Image Classification
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Li Fuxin
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GANMEX: One-vs-One Attributions Guided by GAN-based Counterfactual Explanation Baselines
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Sheng-Min Shih
Pin-Ju Tien
Zohar Karnin
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MAIRE -- A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers
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Shweta Jain
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157
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Receptive Field Size Optimization with Continuous Time Pooling
IEEE Workshop/Winter Conference on Applications of Computer Vision (WACV), 2020
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M. Peto
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Machine versus Human Attention in Deep Reinforcement Learning Tasks
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Ruohan Zhang
Bo Liu
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Peter Stone
OffRL
339
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29 Oct 2020
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