Methods > Computer Vision > Generative Models

Bidirectional GAN

Introduced by Donahue et al. in Adversarial Feature Learning

A BiGAN, or Bidirectional GAN, is a type of generative adversarial network where the generator not only maps latent samples to generated data, but also has an inverse mapping from data to the latent representation. The motivation is to make a type of GAN that can learn rich representations for us in applications like unsupervised learning.

In addition to the generator $G$ from the standard GAN framework, BiGAN includes an encoder $E$ which maps data $\mathbf{x}$ to latent representations $\mathbf{z}$. The BiGAN discriminator $D$ discriminates not only in data space ($\mathbf{x}$ versus $G\left(\mathbf{z}\right)$), but jointly in data and latent space (tuples $\left(\mathbf{x}, E\left(\mathbf{x}\right)\right)$ versus $\left(G\left(z\right), z\right)$), where the latent component is either an encoder output $E\left(\mathbf{x}\right)$ or a generator input $\mathbf{z}$.

Source: Adversarial Feature Learning

Latest Papers

PAPER DATE
Combining GANs and AutoEncoders for Efficient Anomaly Detection
| Fabio CarraraGiuseppe AmatoLuca BrombinFabrizio FalchiClaudio Gennaro
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Generalized Adversarially Learned Inference
Yatin DandiHomanga BharadhwajAbhishek KumarPiyush Rai
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Bidirectional Generative Modeling Using Adversarial Gradient Estimation
| Xinwei ShenTong ZhangKani Chen
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| Yuki M. AsanoChristian RupprechtAndrea Vedaldi
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Paul K. RubensteinYunpeng LiDominik Roblek
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Maciej ZamorskiMaciej Zięba
2018-11-28
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Shengjia ZhaoJiaming SongStefano Ermon
2018-01-01
Theoretical limitations of Encoder-Decoder GAN architectures
Sanjeev AroraAndrej RisteskiYi Zhang
2017-11-07
Multi-view Generative Adversarial Networks
Mickaël ChenLudovic Denoyer
2016-11-07
Adversarial Feature Learning
| Jeff DonahuePhilipp KrähenbühlTrevor Darrell
2016-05-31

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