Generative18 A few more deep discrete Generative Models
23 Aug 2018 5Generative 9DiscreteApp generative generalization GAN discrete Amortized Autoencoder Variational programPresenter  Papers  Paper URL  Our Slides 

Arshdeep  The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh ^{1}  
GaoJi  Summary Of Several Autoencoder models  
GaoJi  Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts ^{2}  
GaoJi  Summary of A Few Recent Papers about Discrete Generative models, SeqGAN, MaskGAN, BEGAN, BoundaryGAN  
Arshdeep  SemiAmortized Variational Autoencoders, Yoon Kim, Sam Wiseman, Andrew C. Miller, David Sontag, Alexander M. Rush ^{3}  
Arshdeep  Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals ^{4} 

_{ Synthesizing Programs for Images using Reinforced Adversarial Learning, Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, S.M. Ali Eslami, Oriol Vinyals / ICML18/ Advances in deep generative networks have led to impressive results in recent years. Nevertheless, such models can often waste their capacity on the minutiae of datasets, presumably due to weak inductive biases in their decoders. This is where graphics engines may come in handy since they abstract away lowlevel details and represent images as highlevel programs. Current methods that combine deep learning and renderers are limited by handcrafted likelihood or distance functions, a need for large amounts of supervision, or difficulties in scaling their inference algorithms to richer datasets. To mitigate these issues, we present SPIRAL, an adversarially trained agent that generates a program which is executed by a graphics engine to interpret and sample images. The goal of this agent is to fool a discriminator network that distinguishes between real and rendered data, trained with a distributed reinforcement learning setup without any supervision. A surprising finding is that using the discriminator’s output as a reward signal is the key to allow the agent to make meaningful progress at matching the desired output rendering. To the best of our knowledge, this is the first demonstration of an endtoend, unsupervised and adversarial inverse graphics agent on challenging real world (MNIST, Omniglot, CelebA) and synthetic 3D datasets. } ↩

_{ Feedback GAN (FBGAN) for DNA: a Novel FeedbackLoop Architecture for Optimizing Protein Functions / Anvita Gupta, James Zou (arxiv Submitted on 5 Apr 2018) / Generative Adversarial Networks (GANs) represent an attractive and novel approach to generate realistic data, such as genes, proteins, or drugs, in synthetic biology. Here, we apply GANs to generate synthetic DNA sequences encoding for proteins of variable length. We propose a novel feedbackloop architecture, called Feedback GAN (FBGAN), to optimize the synthetic gene sequences for desired properties using an external function analyzer. The proposed architecture also has the advantage that the analyzer need not be differentiable. We apply the feedbackloop mechanism to two examples: 1) generating synthetic genes coding for antimicrobial peptides, and 2) optimizing synthetic genes for the secondary structure of their resulting peptides. A suite of metrics demonstrate that the GAN generated proteins have desirable biophysical properties. The FBGAN architecture can also be used to optimize GANgenerated datapoints for useful properties in domains beyond genomics. } ↩

_{ The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables, Chris J. Maddison, Andriy Mnih, Yee Whye Teh (2016)/ The reparameterization trick enables optimizing large scale stochastic computation graphs via gradient descent. The essence of the trick is to refactor each stochastic node into a differentiable function of its parameters and a random variable with fixed distribution. After refactoring, the gradients of the loss propagated by the chain rule through the graph are low variance unbiased estimators of the gradients of the expected loss. While many continuous random variables have such reparameterizations, discrete random variables lack useful reparameterizations due to the discontinuous nature of discrete states. In this work we introduce Concrete random variables—continuous relaxations of discrete random variables. The Concrete distribution is a new family of distributions with closed form densities and a simple reparameterization. Whenever a discrete stochastic node of a computation graph can be refactored into a onehot bit representation that is treated continuously, Concrete stochastic nodes can be used with automatic differentiation to produce lowvariance biased gradients of objectives (including objectives that depend on the logprobability of latent stochastic nodes) on the corresponding discrete graph. We demonstrate the effectiveness of Concrete relaxations on density estimation and structured prediction tasks using neural networks. } ↩

_{ Latent Constraints: Learning to Generate Conditionally from Unconditional Generative Models, Jesse Engel, Matthew Hoffman, Adam Roberts , arxiv 2017/ Deep generative neural networks have proven effective at both conditional and unconditional modeling of complex data distributions. Conditional generation enables interactive control, but creating new controls often requires expensive retraining. In this paper, we develop a method to condition generation without retraining the model. By posthoc learning latent constraints, value functions that identify regions in latent space that generate outputs with desired attributes, we can conditionally sample from these regions with gradientbased optimization or amortized actor functions. Combining attribute constraints with a universal “realism” constraint, which enforces similarity to the data distribution, we generate realistic conditional images from an unconditional variational autoencoder. Further, using gradientbased optimization, we demonstrate identitypreserving transformations that make the minimal adjustment in latent space to modify the attributes of an image. Finally, with discrete sequences of musical notes, we demonstrate zeroshot conditional generation, learning latent constraints in the absence of labeled data or a differentiable reward function. Code with dedicated cloud instance has been made publicly available. } ↩