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BiGAN

Bidirectional GAN

GeneralIntroduced 200015 papers

Description

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 $\mathbf{z}$ .

Papers Using This Method

Adventurer: Exploration with BiGAN for Deep Reinforcement Learning2025-03-24 Improving GANs for Long-Tailed Data through Group Spectral Regularization2022-08-21 Enhancement to Training of Bidirectional GAN : An Approach to Demystify Tax Fraud2022-08-16 GM Score: Incorporating inter-class and intra-class generator diversity, discriminability of disentangled representation, and sample fidelity for evaluating GANs2021-12-13 Generative Adversarial Networks and Adversarial Autoencoders: Tutorial and Survey2021-11-26 Combining GANs and AutoEncoders for Efficient Anomaly Detection2020-11-16 Generalized Adversarially Learned Inference2020-06-15 Bidirectional Generative Modeling Using Adversarial Gradient Estimation2020-02-21 A critical analysis of self-supervision, or what we can learn from a single image2019-04-30 An Empirical Study of Generative Models with Encoders2018-12-19 Semi-supervised learning with Bidirectional GANs2018-11-28 The Information-Autoencoding Family: A Lagrangian Perspective on Latent Variable Generative Modeling2018-01-01 Theoretical limitations of Encoder-Decoder GAN architectures2017-11-07 Multi-view Generative Adversarial Networks2016-11-07 Adversarial Feature Learning2016-05-31