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Humans are able to precisely communicate diverse concepts by employing sketches, a highly reduced and abstract shape based representation of visual content.
Query by visual example - content based image retrieval
K. Hirata and T. Kato · 1992
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Image quality assessment: from error visibility to structural similarity
Z. Wang, A. Bovik, H. Rahim Sheikh, and E. Simoncelli · 2004
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How do humans sketch objects?
M. Eitz, J. Hays, and M. Alexa · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Xdog: an extended difference-of-gaussians compendium including advanced image stylization
H. WinnemöLler, J. E. Kyprianidis, and S. C. Olsen · 2012
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Efficient estimation of word representations in vector space
T. Mikolov, K. Chen, G. Corrado, and J. Dean · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Adam: A method for stochastic optimization
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Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
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Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
K. He, X. Zhang, S. Ren, and J. Sun · 2015
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Learning deconvolution network for semantic segmentation
H. Noh, S. Hong, and B. Han · 2015
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U-Net: Convolutional Networks for Biomedical Image Segmentation
O. Ronneberger, P. Fischer, and T. Brox · 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, A. C. Berg, and L. Fei-Fei · 2015
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 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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Holistically-nested edge detection
S. Xie and Z. Tu · 2015
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Generating images with perceptual similarity metrics based on deep networks
A. Dosovitskiy and T. Brox · 2016
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Deep neural networks as a computational model for human shape sensitivity
J. Kubilius, S. Bracci, and H. P. O. de Beeck · 2016
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Image-to-image translation with conditional adversarial networks
P. Isola, J.-Y. Zhu, T. Zhou, and A. A. Efros · 2017
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Learning to generate images with perceptual similarity metrics
K. Ridgeway, J. Snell, B. Roads, R. S. Zemel, and M. C. Mozer · 2017
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Temporal generative adversarial nets with singular value clipping
M. Saito, E. Matsumoto, and S. Saito · 2017
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Sketch-a-Net: A deep neural network that beats humans
Q. Yu, Y. Yang, F. Liu, Y.-Z. Song, T. Xiang, and T. M. Hospedales · 2017
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Unsupervised learning of depth and ego-motion from video
T. Zhou, M. Brown, N. Snavely, and D. G. Lowe · 2017
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SketchyGAN: Towards diverse and realistic sketch to image synthesis
W. Chen and J. Hays · 2018
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Autoencoding beyond pixels using a learned similarity metric
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther · 2016
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The Sketchy Database: Learning to Retrieve Badly Drawn Bunnies
P. Sangkloy, N. Burnell, C. Ham, and J. Hays · 2016
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Rethinking the inception architecture for computer vision
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna · 2016
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Unsupervised monocular depth estimation with left-right consistency
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X. Huang and S. Belongie · 2017
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Flownet 2.0: Evolution of optical flow estimation with deep networks
E. Ilg, N. Mayer, T. Saikia, M. Keuper, A. Dosovitskiy, and T. Brox · 2017
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A neural representation of sketch drawings
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A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2018
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Learning deep sketch abstraction
U. R. Muhammad, Y. Yang, Y.-Z. Song, T. Xiang, and T. M. Hospedales · 2018
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Generative image inpainting with contextual attention
J. Yu, Z. Lin, J. Yang, X. Shen, X. Lu, and T. S. Huang · 2018
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The unreasonable effectiveness of deep features as a perceptual metric
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, and O. Wang · 2018
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Imagenet-trained cnns are biased towards texture; increasing shape bias improves accuracy and robustness
R. Geirhos, P. Rubisch, C. Michaelis, M. Bethge, F. A. Wichmann, and W. Brendel · 2019
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