BigGAN is a type of generative adversarial network that was designed for scaling generation to high-resolution, high-fidelity images. It includes a number of incremental changes and innovations. The baseline and incremental changes are:

  • Using SAGAN as a baseline with spectral norm. for G and D, and using TTUR.
  • Using a Hinge Loss GAN objective
  • Using class-conditional batch normalization to provide class information to G (but with linear projection not MLP.
  • Using a projection discriminator for D to provide class information to D.
  • Evaluating with EWMA of G's weights, similar to ProGANs.

The innovations are:

  • Increasing batch sizes, which has a big effect on the Inception Score of the model.
  • Increasing the width in each layer leads to a further Inception Score improvement.
  • Adding skip connections from the latent variable $z$ to further layers helps performance.
  • A new variant of Orthogonal Regularization.
Source: Large Scale GAN Training for High Fidelity Natural Image Synthesis

Latest Papers

PAPER DATE
Conditioning Trick for Training Stable GANs
Mohammad EsmaeilpourRaymel Alfonso SalloOlivier St-GeorgesPatrick CardinalAlessandro Lameiras Koerich
2020-10-12
SMYRF: Efficient Attention using Asymmetric Clustering
| Giannis DarasNikita KitaevAugustus OdenaAlexandros G. Dimakis
2020-10-11
TinyGAN: Distilling BigGAN for Conditional Image Generation
Ting-Yun ChangChi-Jen Lu
2020-09-29
not-so-BigGAN: Generating High-Fidelity Images on a Small Compute Budget
Seungwook HanAkash SrivastavaCole HurwitzPrasanna SattigeriDavid D. Cox
2020-09-09
Neural Crossbreed: Neural Based Image Metamorphosis
Sanghun ParkKwanggyoon SeoJunyong Noh
2020-09-02
Instance Selection for GANs
Terrance DeVriesMichal DrozdzalGraham W. Taylor
2020-07-30
Interpolating GANs to Scaffold Autotelic Creativity
Ziv EpsteinOcéane BoulaisSkylar GordonMatt Groh
2020-07-21
Differentiable Augmentation for Data-Efficient GAN Training
| Shengyu ZhaoZhijian LiuJi LinJun-Yan ZhuSong Han
2020-06-18
Training Generative Adversarial Networks with Limited Data
| Tero KarrasMiika AittalaJanne HellstenSamuli LaineJaakko LehtinenTimo Aila
2020-06-11
Learning disconnected manifolds: a no GANs land
Ugo TanielianThibaut IssenhuthElvis DohmatobJeremie Mary
2020-06-08
Big GANs Are Watching You: Towards Unsupervised Object Segmentation with Off-the-Shelf Generative Models
| Andrey VoynovStanislav MorozovArtem Babenko
2020-06-08
A U-Net Based Discriminator for Generative Adversarial Networks
Edgar Schonfeld Bernt Schiele Anna Khoreva
2020-06-01
Network Fusion for Content Creation with Conditional INNs
Robin RombachPatrick EsserBjörn Ommer
2020-05-27
GANSpace: Discovering Interpretable GAN Controls
| Erik HärkönenAaron HertzmannJaakko LehtinenSylvain Paris
2020-04-06
Evolving Normalization-Activation Layers
| Hanxiao LiuAndrew BrockKaren SimonyanQuoc V. Le
2020-04-06
Feature Quantization Improves GAN Training
| Yang ZhaoChunyuan LiPing YuJianfeng GaoChangyou Chen
2020-04-05
BigGAN-based Bayesian reconstruction of natural images from human brain activity
Kai QiaoJian ChenLinyuan WangChi ZhangLi TongBin Yan
2020-03-13
A U-Net Based Discriminator for Generative Adversarial Networks
| Edgar SchönfeldBernt SchieleAnna Khoreva
2020-02-28
Improved Consistency Regularization for GANs
Zhengli ZhaoSameer SinghHonglak LeeZizhao ZhangAugustus OdenaHan Zhang
2020-02-11
Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN
Milad MozafariLeila ReddyRufin VanRullen
2020-01-31
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
Mohamed El Amine SeddikCosme LouartMohamed TamaazoustiRomain Couillet
2020-01-21
CNN-generated images are surprisingly easy to spot... for now
| Sheng-Yu WangOliver WangRichard ZhangAndrew OwensAlexei A. Efros
2019-12-23
Detecting GAN generated errors
Xiru ZhuFengdi CheTianzi YangTzuyang YuDavid MegerGregory Dudek
2019-12-02
Semantic Hierarchy Emerges in Deep Generative Representations for Scene Synthesis
| Ceyuan YangYujun ShenBolei Zhou
2019-11-21
Improving sample diversity of a pre-trained, class-conditional GAN by changing its class embeddings
| Qi LiLong MaiMichael A. AlcornAnh Nguyen
2019-10-10
Adversarial Video Generation on Complex Datasets
Aidan ClarkJeff DonahueKaren Simonyan
2019-07-15
Large Scale Adversarial Representation Learning
| Jeff DonahueKaren Simonyan
2019-07-04
Improved Precision and Recall Metric for Assessing Generative Models
| Tuomas KynkäänniemiTero KarrasSamuli LaineJaakko LehtinenTimo Aila
2019-04-15
High-Fidelity Image Generation With Fewer Labels
| Mario LucicMichael TschannenMarvin RitterXiaohua ZhaiOlivier BachemSylvain Gelly
2019-03-06
Metropolis-Hastings view on variational inference and adversarial training
Kirill NeklyudovEvgenii EgorovPavel ShvechikovDmitry Vetrov
2018-10-16
Large Scale GAN Training for High Fidelity Natural Image Synthesis
| Andrew BrockJeff DonahueKaren Simonyan
2018-09-28

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