Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
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This is how Generative Adversarial Networks (GANs) work and benefit the tech and dev industry. Although GANs still have room for improvement, GANs are important generative models that learn how to create realistic samples. GANS Ian Goodfellow, OpenAI Research Scientist
Generative Adversarial Networks (GANs) - Ian Goodfellow, OpenAI
- 1. Generative Adversarial Networks (GANs) Ian Goodfellow, OpenAI Research Scientist Presentation at AI With the Best, 2016-09-24
- 2. (Goodfellow 2016) Generative Modeling • Density estimation • Sample generation Training examples Model samples
- 3. (Goodfellow 2016) Adversarial Nets Framework Input noise Z Differentiable function G x sampled from model Differentiable function D D tries to output 0 x sampled from data Differentiable function D D tries to output 1
- 4. (Goodfellow 2016) DCGAN Architecture (Radford et al 2015) Most “deconvs” are batch normalized
- 5. (Goodfellow 2016) DCGANs for LSUN Bedrooms (Radford et al 2015)
- 6. (Goodfellow 2016) Vector Space ArithmeticCHAPTER 15. REPRESENTATION LEARNING - + = Figure 15.9: A generative model has learned a distributed representation that disentangl he concept of gender from the concept of wearing glasses. If we begin with the repr entation of the concept of a man with glasses, then subtract the vector representing th oncept of a man without glasses, and finally add the vector representing the conce f a woman without glasses, we obtain the vector representing the concept of a woma Man with glasses Man Woman Woman with Glasses
- 7. (Goodfellow 2016) Mode Collapse • Fully optimizing the discriminator with the generator held constant is safe • Fully optimizing the generator with the discriminator held constant results in mapping all points to the argmax of the discriminator • Can partially fix this by adding nearest-neighbor features constructed from the current minibatch to the discriminator (“minibatch GAN”) (Salimans et al 2016)
- 8. (Goodfellow 2016) Minibatch GAN on CIFAR Training Data Samples (Salimans et al 2016)
- 9. (Goodfellow 2016) Minibatch GAN on ImageNet (Salimans et al 2016)
- 11. (Goodfellow 2016) Text to Image with GANs n Yan, Lajanugen Logeswaran REEDSCOT1 , AKATA2 , XCYAN1 , LLAJAN1 SCHIELE2 ,HONGLAK1 r, MI, USA (UMICH.EDU) cs, Saarbr¨ucken, Germany (MPI-INF.MPG.DE) images from text l, but current AI oal. However, in ful recurrent neu- been developed ature representa- utional generative ave begun to gen- s of specific cat- covers, and room elop a novel deep ion to effectively nd image model- s from characters capability of our ages of birds and iptions. this small bird has a pink breast and crown, and black primaries and secondaries. the flower has petals that are bright pinkish purple with white stigma this magnificent fellow is almost all black with a red crest, and white cheek patch. this white and yellow flower have thin white petals and a round yellow stamen Figure 1. Examples of generated images from text descriptions.(Reed et al 2016)
- 13. (Goodfellow 2016) Single Image Super-Resolution (Ledig et al 2016)
- 14. (Goodfellow 2016) iGAN youtube (Zhu et al 2016)
- 16. (Goodfellow 2016) Conclusion • GANs are generative models based on supervised learning and game theory • GANs learn to generate realistic samples • Like other generative models, GANs still need a lot of improvement