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Inverting The Generator Of A Generative Adversarial Network · Around

2016

Inverting The Generator Of A Generative Adversarial Network

Creswell, Antonia, Bharath, Anil Anthony

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Understand

Generative adversarial networks (GANs) learn to synthesise new samples from a high-dimensional distribution by passing samples drawn from a latent space through a generative network.

When the high-dimensional distribution describes images of a particular data set, the network should learn to generate visually similar image samples for latent variables that are close to each other in the latent space.
For tasks such as image retrieval and image classification, it may be useful to exploit the arrangement of the latent space by projecting images into it, and using this as a representation for discriminative tasks.
GANs often consist of multiple layers of non-linear computations, making them very difficult to invert.

Built on

“Generative adversarial nets”
Ian Goodfellow et al · 2014
Earlier work this paper cites.
“Long-term recurrent convolutional networks for visual recognition and description”
Jeffrey Donahue et al · 2015
Earlier work this paper cites.
“Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift”
Sergey Ioffe and Christian Szegedy · 2015
Earlier work this paper cites.
“Human-level concept learning through probabilistic program induction”
Brenden˜M Lake, Ruslan Salakhutdinov and Joshua˜B Tenenbaum · 2015
Earlier work this paper cites.

Similar

“Understanding deep image representations by inverting them”
Aravindh Mahendran and Andrea Vedaldi · 2015
Cited alongside, same era.
“InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets”
Xi Chen et al · 2016
Cited alongside, same era.
“Task Specific Adversarial Cost Function”
Original
Antonia Creswell and Anil˜A Bharath · 2016
Cited alongside, same era.
“Adversarial Feature Learning”
Original
Jeff Donahue, Philipp Kr\"ahenb\"uhl and Trevor Darrell · 2016
Cited alongside, same era.

Then

“Adversarially Learned Inference”
Original
Vincent Dumoulin et al · 2016
Closest in time.
“Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks”
Alec Radford, Luke Metz and Soumith Chintala · 2016
Closest in time.
“Improved Techniques for Training GANs”
Tim Salimans et al · 2016
Closest in time.
“Generative Visual Manipulation on the Natural Image Manifold”
Jun-Yan Zhu, Philipp Kr\"ahenb\"uhl, Eli Shechtman and Alexei˜A Efros · 2016
Closest in time.

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