Fetching the paper…
Reading the bibliography…
This paper proposes a method to modify traditional convolutional neural networks (CNNs) into interpretable CNNs, in order to clarify knowledge representations in high conv-layers of CNNs.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
The caltech-ucsd birds-200-2011 dataset
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie · 2011
Earlier work this paper cites.
Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. Hinton · 2012
Earlier work this paper cites.
Unsupervised discovery of mid-level discriminative patches
S. Singh, A. Gupta, and A. A. Efros · 2012
Earlier work this paper cites.
Deep inside convolutional networks: visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2013
Earlier work this paper cites.
Detect what you can: Detecting and representing objects using holistic models and body parts
X. Chen, R. Mottaghi, X. Liu, S. Fidler, R. Urtasun, and A. Yuille · 2014
Earlier work this paper cites.
Part detector discovery in deep convolutional neural networks
M. Simon, E. Rodner, and J. Denzler · 2014
Earlier work this paper cites.
Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
Understanding deep features with computer-generated imagery
M. Aubry and B. C. Russell · 2015
Earlier work this paper cites.
Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
Earlier work this paper cites.
Neural activation constellations: Unsupervised part model discovery with convolutional networks
M. Simon and E. Rodner · 2015
Earlier work this paper cites.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Object detectors emerge in deep scene cnns
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2015
Cited alongside, same era.
Inverting visual representations with convolutional networks
A. Dosovitskiy and T. Brox · 2016
Cited alongside, same era.
Towards transparent ai systems: Interpreting visual question answering models
Y. Goyal, A. Mohapatra, D. Parikh, and D. Batra · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Explaining distributed neural activations via unsupervised learning
S. Kolouri, C. E. Martin, and H. Hoffmann · 2017
Closest in time.
Explaining the unexplained: A class-enhanced attentive response (clear) approach to understanding deep neural networks
D. Kumar, A. Wong, and G. W. Taylor · 2017
Closest in time.
Identifying unknown unknowns in the open world: Representations and policies for guided exploration
H. Lakkaraju, E. Kamar, R. Caruana, and E. Horvitz · 2017
Closest in time.
Visual explanations for convolutional neural networks via input resampling
B. J. Lengerich, S. Konam, E. P. Xing, S. Rosenthal, and M. Veloso · 2017
Closest in time.
Right for the right reasons: Training differentiable models by constraining their explanations
A. S. Ross, M. C. Hughes, and F. Doshi-Velez · 2017
Closest in time.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Z. Hu, X. Ma, Z. Liu, E. Hovy, and E. P. Xing · 2016
Cited alongside, same era.
“why should i trust you?” explaining the predictions of any classifier
M. T. Ribeiro, S. Singh, and C. Guestrin · 2016
Cited alongside, same era.
Growing interpretable graphs on convnets via multi-shot learning
Q. Zhang, R. Cao, Y. N. Wu, and S.-C. Zhu · 2016
Cited alongside, same era.
Learning deep features for discriminative localization
B. Zhou, A. Khosla, A. Lapedriza, A. Oliva, and A. Torralba · 2016
Cited alongside, same era.
Network dissection: Quantifying interpretability of deep visual representations
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba · 2017
Cited alongside, same era.
Interpretable explanations of black boxes by meaningful perturbation
R. C. Fong and A. Vedaldi · 2017
Cited alongside, same era.
Decoding the deep: Exploring class hierarchies of deep representations using multiresolution matrix factorization
V. K. Ithapu · 2017
Cited alongside, same era.
Dynamic routing between capsules
S. Sabour, N. Frosst, and G. E. Hinton · 2017
Closest in time.
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 · 2017
Closest in time.
Teaching compositionality to cnns
A. Stone, H. Wang, Y. Liu, D. S. Phoenix, and D. George · 2017
Closest in time.
Interpreting cnn models for apparent personality trait regression
C. Ventura, D. Masip, and A. Lapedriza · 2017
Closest in time.
Human-explainable features for job candidate screening prediction
A. S. Wicaksana and C. C. S. Liem · 2017
Closest in time.
New theory cracks open the black box of deep learning
N. Wolchover · 2017
Closest in time.
Interpreting cnn knowledge using an explanatory graph
Q. Zhang, R. Cao, F. Shi, Y. Wu, and S.-C. Zhu · 2017
Closest in time.
Mining part concepts from cnns via active question-answering
Q. Zhang, R. Cao, Y. N. Wu, and S.-C. Zhu · 2017
Closest in time.