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We propose an interactive image-manipulation system with natural language instruction, which can generate a target image from a source image and an instruction that describes the difference between the source and the target image.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber · 1997
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Image quality assessment: from error visibility to structural similarity
Zhou Wang, Alan C Bovik, Hamid R Sheikh, and Eero P Simoncelli · 2004
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Auto-encoding variational bayes
Diederik Kingma and Max Welling · 2014
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Unifying visual-semantic embeddings with multimodal neural language models
Ryan Kiros, Ruslan Salakhutdinov, and Richard S Zemel · 2014
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Elman Mansimov, Emilio Parisotto, Jimmy Ba, and Ruslan Salakhutdinov · 2016
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2016
Unsupervised representation learning with deep convolutional generative adversarial networks
Alec Radford, Luke Metz, and Soumith Chintala · 2016
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Generative adversarial text-to-image synthesis
Scott Reed, Zeynep Akata, Xinchen Yan, Lajanugen Logeswaran, Bernt Schiele, and Honglak Lee · 2016
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Improved techniques for training gans
Tim Salimans, Ian Goodfellow, Wojciech Zaremba, Vicki Cheung, Alec Radford, and Xi Chen · 2016
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Conditional image generation with pixelcnn decoders
Aaron van den Oord, Nal Kalchbrenner, Lasse Espeholt, Oriol Vinyals, Alex Graves, et al · 2016
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Generative visual manipulation on the natural image manifold
Jun-Yan Zhu, Philipp Krähenbühl, Eli Shechtman, and Alexei A. Efros · 2016
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Cited alongside, same era.