Differentiable Generator Nets
Deep Learning
Srihari
Differentiable Generator Nets
Sargur N. Srihari srihari@cedar.buffalo.edu
Topics in Deep Learning Deep GSrieharinerative Models
1. Boltzmann machines
2. Restricted Boltzmann machines 3. Deep Belief Networks 4. Deep Boltzmann machines 5. Boltzmann machines for continuous data 6. Convolutional Boltzmann machines 7. Boltzmann machines for structured and sequential outputs 8. Other Boltzmann machines
9. Backpropagation through random operations 10. Directed generative nets
11. Drawing samples from autoencoders 12. Generative stochastic networks
13. Other generative schemes
14. Evaluating generative models
15. Conclusion
2
Deep Learning
Topics in DirecteSdrihari Generative Nets
1. Sigmoid Belief Networks 2. Differentiable generator nets 3. Variational Autoencoders 4. Generative Adversarial Networks 5. Generative Moment Matching Networks 6. Convolutional Generative Networks 7. Autoregressive Networks 8. Linear Auto-Regressive Networks 9. Neural Auto-Regressive Networks 10.NADE
3
Deep Learning
Differentiable GSriharienerator Nets
? Many generative models are based on idea of using a differentiable generator network
? The model transforms samples of latent variables z to samples x or to distributions over samples x using a differentiable function
? g(z;(g)) which is represented by a neural network
4
Deep Learning
Differentiable GSriharienerator Nets
? This model class includes
1.Variational autoencoders (VAE)
? Which pair the generator network with an inference net
2.Generative Adversarial networks (GAN)
? Which pair the generator network with a discriminator network
3.Techniques that train isolated generator networks
VAE
GAN
Without/with reparametrization
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