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As deep reinforcement learning driven by visual perception becomes more widely used there is a growing need to better understand and probe the learned agents.
Gradient-based learning applied to document recognition
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Imagenet classification with deep convolutional neural networks
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
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Do convnets learn correspondence?
J. L. Long, N. Zhang, and T. Darrell · 2014
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Striving for simplicity: The all convolutional net
J. T. Springenberg, A. Dosovitskiy, T. Brox, and M. Riedmiller · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Inverting convolutional networks with convolutional networks
A. Dosovitskiy and T. Brox · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
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Fully convolutional networks for semantic segmentation
J. Long, E. Shelhamer, and T. Darrell · 2015
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Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images
A. Nguyen, J. Yosinski, and J. Clune · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
S. Ren, K. He, R. Girshick, and J. Sun · 2015
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Show, attend and tell: Neural image caption generation with visual attention
K. Xu, J. Ba, R. Kiros, K. Cho, A. Courville, R. Salakhudinov, R. Zemel, and Y. Bengio · 2015
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Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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A taxonomy and library for visualizing learned features in convolutional neural networks
F. Grün, C. Rupprecht, N. Navab, and F. Tombari · 2016
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Visualizing and understanding recurrent networks
A. Karpathy, J. Johnson, and L. Fei-Fei · 2016
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Visualizing deep convolutional neural networks using natural pre-images
A. Mahendran and A. Vedaldi · 2016
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Asynchronous methods for deep reinforcement learning
Visualizing and understanding atari agents
S. Greydanus, A. Koul, J. Dodge, and A. Fern · 2017
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The (un) reliability of saliency methods
P.-J. Kindermans, S. Hooker, J. Adebayo, M. Alber, K. T. Schütt, S. Dähne, D. Erhan, and B. Kim · 2017
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Patternnet and patternlrp–improving the interpretability of neural networks
P.-J. Kindermans, K. T. Schütt, M. Alber, K.-R. Müller, and S. Dähne · 2017
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Feature visualization
C. Olah, A. Mordvintsev, and L. Schubert · 2017
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Axiomatic attribution for deep networks
M. Sundararajan, A. Taly, and Q. Yan · 2017
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V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 2016
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Synthesizing the preferred inputs for neurons in neural networks via deep generator networks
A. Nguyen, A. Dosovitskiy, J. Yosinski, T. Brox, and J. Clune · 2016
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Plug & play generative networks: Conditional iterative generation of images in latent space
A. Nguyen, J. Yosinski, Y. Bengio, A. Dosovitskiy, and J. Clune · 2016
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A. Nguyen, J. Yosinski, and J. Clune · 2016
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Grad-cam: Visual explanations from deep networks via gradient-based localization
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra · 2016
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Dueling network architectures for deep reinforcement learning
Z. Wang, T. Schaul, M. Hessel, H. Van Hasselt, M. Lanctot, and N. De Freitas · 2016
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Graying the black box: Understanding dqns
T. Zahavy, N. Ben-Zrihem, and S. Mannor · 2016
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D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2017
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Scalable trust-region method for deep reinforcement learning using kronecker-factored approximation
Y. Wu, E. Mansimov, R. B. Grosse, S. Liao, and J. Ba · 2017
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Visualizing deep neural network decisions: Prediction difference analysis
L. M. Zintgraf, T. S. Cohen, T. Adel, and M. Welling · 2017
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Local explanation methods for deep neural networks lack sensitivity to parameter values
J. Adebayo, J. Gilmer, I. Goodfellow, and B. Kim · 2018
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Sanity checks for saliency maps
J. Adebayo, J. Gilmer, M. Muelly, I. Goodfellow, M. Hardt, and B. Kim · 2018
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Understanding regularization to visualize convolutional neural networks
M. Baust, F. Ludwig, C. Rupprecht, M. Kohl, and S. Braunewell · 2018
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Grad-cam++: Generalized gradient-based visual explanations for deep convolutional networks
A. Chattopadhay, A. Sarkar, P. Howlader, and V. N. Balasubramanian · 2018
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Evaluating feature importance estimates
S. Hooker, D. Erhan, P.-J. Kindermans, and B. Kim · 2018
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Establishing appropriate trust via critical states
S. H. Huang, K. Bhatia, P. Abbeel, and A. D. Dragan · 2018
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Pytorch implementations of reinforcement learning algorithms
I. Kostrikov · 2018
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The building blocks of interpretability
C. Olah, A. Satyanarayan, I. Johnson, S. Carter, L. Schubert, K. Ye, and A. Mordvintsev · 2018
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