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In this paper, we examine the visual variability of objects across different ad categories, i.e.
Coding, analysis, interpretation, and recognition of facial expressions
Irfan A. Essa and Alex Paul Pentland · 1997
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Comprehensive database for facial expression analysis
Takeo Kanade, Jeffrey F Cohn, and Yingli Tian · 2000
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Distinctive image features from scale-invariant keypoints
David G Lowe · 2004
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Reducing the dimensionality of data with neural networks
Geoffrey E Hinton and Ruslan R Salakhutdinov · 2006
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Describing objects by their attributes
Ali Farhadi, Ian Endres, Derek Hoiem, and David Forsyth · 2009
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Facial expression recognition based on local binary patterns: A comprehensive study
Caifeng Shan, Shaogang Gong, and Peter W McOwan · 2009
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Understanding the difficulty of training deep feedforward neural networks
Xavier Glorot and Yoshua Bengio · 2010
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Imagenet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Visual persuasion: Inferring communicative intents of images
Jungseock Joo, Weixin Li, Francis F Steen, and Song-Chun Zhu · 2014
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Auto-encoding variational bayes
Diederik P Kingma and Max Welling · 2014
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Attribute-based classification for zero-shot visual object categorization
Christoph H Lampert, Hannes Nickisch, and Stefan Harmeling · 2014
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Microsoft coco: Common objects in context
Tsung-Yi Lin, Michael Maire, Serge Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C Lawrence Zitnick · 2014
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Facial expression recognition via a boosted deep belief network
Ping Liu, Shizhong Han, Zibo Meng, and Yan Tong · 2014
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Delving deep into rectifiers: Surpassing human-level performance on imagenet classification
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
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Automated facial trait judgment and election outcome prediction: Social dimensions of face
Jungseock Joo, Francis F Steen, and Song-Chun Zhu · 2015
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Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2015
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Deep learning face attributes in the wild
Ziwei Liu, Ping Luo, Xiaogang Wang, and Xiaoou Tang · 2015
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Faster r-cnn: Towards real-time object detection with region proposal networks
Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun · 2015
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Very deep convolutional networks for large-scale image recognition
Karen Simonyan and Andrew Zisserman · 2015
Began: Boundary equilibrium generative adversarial networks
David Berthelot, Tom Schumm, and Luke Metz · 2017
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Deligan: Generative adversarial networks for diverse and limited data
Swaminathan Gurumurthy, Ravi Kiran Sarvadevabhatla, and R Venkatesh Babu · 2017
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Deep feature consistent variational autoencoder
Xianxu Hou, Linlin Shen, Ke Sun, and Guoping Qiu · 2017
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Automatic understanding of image and video advertisements
Zaeem Hussain, Mingda Zhang, Xiaozhong Zhang, Keren Ye, Christopher Thomas, Zuha Agha, Nathan Ong, and Adriana Kovashka · 2017
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Fader networks: Manipulating images by sliding attributes
Guillaume Lample, Neil Zeghidour, Nicolas Usunier, Antoine Bordes, Ludovic DENOYER, et al · 2017
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Learning structured output representation using deep conditional generative models
Kihyuk Sohn, Honglak Lee, and Xinchen Yan · 2015
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Alireza Makhzani, Jonathon Shlens, Navdeep Jaitly, and Ian Goodfellow · 2016
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Deep multi-scale video prediction beyond mean square error
Michael Mathieu, Camille Couprie, and Yann LeCun · 2016
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Variational autoencoder for deep learning of images, labels and captions
Yunchen Pu, Zhe Gan, Ricardo Henao, Xin Yuan, Chunyuan Li, Andrew Stevens, and Lawrence Carin · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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An uncertain future: Forecasting from static images using variational autoencoders
Jacob Walker, Carl Doersch, Abhinav Gupta, and Martial Hebert · 2016
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Tsung-Yi Lin, Priya Goyal, Ross Girshick, Kaiming He, and Piotr Dollar · 2017
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Affectnet: A database for facial expression, valence, and arousal computing in the wild
Ali Mollahosseini, Behzad Hasani, and Mohammad H Mahoor · 2017
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Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Jun-Yan Zhu, Taesung Park, Phillip Isola, and Alexei A Efros · 2017
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Yunjey Choi, Minje Choi, Munyoung Kim, Jung-Woo Ha, Sunghun Kim, and Jaegul Choo · 2018
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Exprgan: Facial expression editing with controllable expression intensity
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Advise: Symbolism and external knowledge for decoding advertisements
Keren Ye and Adriana Kovashka · 2018
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The unreasonable effectiveness of deep networks as a perceptual metric
Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang · 2018
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