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We propose a general framework called Network Dissection for quantifying the interpretability of latent representations of CNNs by evaluating the alignment between individual hidden units and a set of semantic concepts.
What is a random matrix?
P. Diaconis · 2005
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Invariant visual representation by single neurons in the human brain
R. Q. Quiroga, L. Reddy, G. Kreiman, C. Koch, and I. Fried · 2005
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Learning color names for real-world applications
J. Van De Weijer, C. Schmid, J. Verbeek, and D. Larlus · 2009
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Human action recognition by learning bases of action attributes and parts
B. Yao, X. Jiang, A. Khosla, A. L. Lin, L. Guibas, and L. Fei-Fei · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
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Intriguing properties of neural networks
C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow, 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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Intrinsic images in the wild
S. Bell, K. Bala, and N. Snavely · 2014
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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
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, and A. Vedaldi · 2014
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The role of context for object detection and semantic segmentation in the wild
R. Mottaghi, X. Chen, X. Liu, N.-G. Cho, S.-W. Lee, S. Fidler, R. Urtasun, and A. Yuille · 2014
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Cnn features off-the-shelf: an astounding baseline for recognition
A. S. Razavian, H. Azizpour, J. Sullivan, and S. Carlsson · 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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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Visualizing and understanding convolutional networks
M. D. Zeiler and R. Fergus · 2014
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Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
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Learning to see by moving
P. Agrawal, J. Carreira, and J. Malik · 2015
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Unsupervised visual representation learning by context prediction
C. Doersch, A. Gupta, and A. A. Efros · 2015
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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
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Do semantic parts emerge in convolutional neural networks?
A. Gonzalez-Garcia, D. Modolo, and V. Ferrari · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
I. Misra, C. L. Zitnick, and M. Hebert · 2016
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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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Learning image representations tied to ego-motion
D. Jayaraman and K. Grauman · 2015
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Convergent learning: Do different neural networks learn the same representations?
Y. Li, J. Yosinski, J. Clune, H. Lipson, and J. Hopcroft · 2015
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Understanding deep image representations by inverting them
A. Mahendran and A. Vedaldi · 2015
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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
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Going deeper with convolutions
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich · 2015
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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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Unsupervised learning of visual representations by solving jigsaw puzzles
M. Noroozi and P. Favaro · 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
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Generating videos with scene dynamics
C. Vondrick, H. Pirsiavash, and A. Torralba · 2016
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S. Zagoruyko and N. Komodakis · 2016
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Colorful image colorization
R. Zhang, P. Isola, and A. A. Efros · 2016
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Places: An image database for deep scene understanding
B. Zhou, A. Khosla, A. Lapedriza, A. Torralba, and A. Oliva · 2016
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
R. Zhang, P. Isola, and A. A. Efros · 2017
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Scene parsing through ade20k dataset
B. Zhou, H. Zhao, X. Puig, S. Fidler, A. Barriuso, and A. Torralba · 2017
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