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In an effort to understand the meaning of the intermediate representations captured by deep networks, recent papers have tried to associate specific semantic concepts to individual neural network filter responses, where interesting correlations are often found, largely by focusing on extremal filter responses.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Building high-level features using large scale unsupervised learning
Q. V. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. S. Corrado, J. Dean, and A. Y. Ng · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Linguistic regularities in continuous space word representations
T. Mikolov, W.-t. Yih, and G. Zweig · 2013
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2013
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Analyzing the performance of multilayer neural networks for object recognition
P. Agrawal, R. Girshick, and J. Malik · 2014
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Unifying visual-semantic embeddings with multimodal neural language models
R. Kiros, R. Salakhutdinov, and R. S. Zemel · 2014
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Deep inside convolutional networks: Visualising image classification models and saliency maps
K. Simonyan, A. Vedaldi, and A. Zisserman · 2014
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, and R. Fergus · 2014
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Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
Cited alongside, same era.
Learning image representations tied to ego-motion
D. Jayaraman and K. Grauman · 2015
Cited alongside, same era.
Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
Cited alongside, same era.
Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
Cited alongside, same era.
Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2015
Cited alongside, same era.
Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
Cited alongside, same era.
Understanding intermediate layers using linear classifier probes
G. Alain and Y. Bengio · 2016
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Object-centric representation learning from unlabeled videos
R. Gao, D. Jayaraman, and K. Grauman · 2016
Later among the works it cites.
Do semantic parts emerge in convolutional neural networks?
A. Gonzalez-Garcia, D. Modolo, and V. Ferrari · 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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Ambient sound provides supervision for visual learning
A. Owens, J. Wu, J. H. McDermott, W. T. Freeman, and A. Torralba · 2016
Later among the works it cites.
Understanding deep learning requires rethinking generalization
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J. Wang, Z. Zhang, C. Xie, V. Premachandran, and A. Yuille · 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.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals · 2016
Later among the works it cites.
Places: An image database for deep scene understanding
B. Zhou, A. Khosla, A. Lapedriza, A. Torralba, and A. Oliva · 2016
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Network dissection: Quantifying interpretability of deep visual representations
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba · 2017
Later among the works it cites.