generator


Can you handle the tooth? AI helps dentists design fake gnashers

#artificialintelligence

Artificially intelligent software has found the next industry to potentially, as the kids say, disrupt – dentistry. A team of researchers from the University of California, Berkeley and Glidewell Dental Lab have built a general adversarial network (GAN) to automatically generate new designs of dental crowns. GANs have been pretty trendy since 2014. There are hundreds of various acronyms for different GAN models like ABC-GAN, CatGAN, DiscoGAN, MAD-GAN, S2GAN, and so and so forth. All of them are made up of a generator and discriminator network duelling against each other.


Python Programming Language LiveLessons

@machinelearnbot

Description In this video training, David Beazley covers the essential features of Python programming through a series of practical programming examples. David assumes you are a programmer, familiar with central programming concepts such as control flow, functions, and data structures. The course provides programmers with an accelerated introduction to the essential parts of Python. Those with some previous Python experience will benefit from being exposed to modern Python style and programming idioms used by experienced programmers. Each lesson covers a "big idea" rather than taking an exhaustive, reference-style approach. In each lesson David addresses a specific, practical problem and demonstrates the solution through code. He explains the steps taken and why he is taking a particular approach.


Build 7 Useful Python Beginner Projects from Scratch

#artificialintelligence

Python is a dynamic modern object -oriented programming language. It is easy to learn and can be used to do a lot of things both big and small. Python is what is referred to as a high level language. That means it is a language that is closer to humans than computer. It is also known as a general purpose programming language due to it's flexibility.


Image-to-image translation with conditional adversarial networks

#artificialintelligence

We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations… As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either. Pix2pix can produce effective results with way fewer training images, and much less training time, than I would have imagined.


ReGAN: RE[LAX|BAR|INFORCE] based Sequence Generation using GANs

arXiv.org Machine Learning

Generative Adversarial Networks (GANs) have seen steep ascension to the peak of ML research zeitgeist in recent years. Mostly catalyzed by its success in the domain of image generation, the technique has seen wide range of adoption in a variety of other problem domains. Although GANs have had a lot of success in producing more realistic images than other approaches, they have only seen limited use for text sequences. Generation of longer sequences compounds this problem. Most recently, SeqGAN (Yu et al., 2017) has shown improvements in adversarial evaluation and results with human evaluation compared to a MLE based trained baseline. The main contributions of this paper are three-fold: 1. We show results for sequence generation using a GAN architecture with efficient policy gradient estimators, 2. We attain improved training stability, and 3. We perform a comparative study of recent unbiased low variance gradient estimation techniques such as REBAR (Tucker et al., 2017), RELAX (Grathwohl et al., 2018) and REINFORCE (Williams, 1992). Using a simple grammar on synthetic datasets with varying length, we indicate the quality of sequences generated by the model.


Generating Pusheen with AI

@machinelearnbot

The BEGAN model uses a loss equation based on the Wasserstein distance, except its goal is to minimize the absolute value of the autoencoder losses on the real and fake images instead of the images themselves. In practice it drops the absolute value and minimizes the reconstruction loss on real images minus the reconstruction loss on fake images. Additionally, it introduces a weighting term on the fake reconstruction loss which changes proportaional to the difference between the fake and real reconstruction losses; this serves to maintain a balance between the discriminator and generator so one does not easily win over the other. I'll be releasing the code soon but to summarize, the architectures and hyperparameters that typically worked well with for the discriminator and generator were: I did a fair amount of hyperparameter searching to get a model that worked (keep scrolling for some failures) but I think it ended up being a pretty decent cat generator. Below is a training video of one of the models that I use in the demos, where every 250 steps I take 16 samples from the generator.


Doom and Super Mario could be a lot tougher now AI is building levels

#artificialintelligence

AI researchers do love their games and two papers have shown that they can use general adversarial networks (GANs) to make old favorites a lot more interesting. In two separate papers, AI researchers built general adversarial networks to construct new video game levels for Super Mario Bros, a popular platform game controlling a mustachioed man in red overalls to collect coins and avoid enemies to reach a princess, and DOOM, the classic first person shooter from the early 1990s. GANs were first introduced in 2014. The system is made up of two networks: a generator and a discriminator. The generator creates fake samples of training data, and a discriminator tries to determine if the samples are real or fake.


Keep Calm and train a GAN. Pitfalls and Tips on training Generative Adversarial Networks

@machinelearnbot

Generative Adversarial Networks (GANs) are among the hottest topics in Deep Learning currently. There has been a tremendous increase in the number of papers being published on GANs over the last several months. GANs have been applied to a great variety of problems and in case you missed the train, here is a list of some cool applications of GANs. Now, I had read a lot about GANs, but never played with one myself. So, after going through some inspiring papers and github repos, I decided to try my hands on training a simple GAN myself and I immediately ran into problems.


Towards Diverse Text Generation with Inverse Reinforcement Learning

arXiv.org Machine Learning

Text generation is a crucial task in NLP. Recently, several adversarial generative models have been proposed to improve the exposure bias problem in text generation. Though these models gain great success, they still suffer from the problems of reward sparsity and mode collapse. In order to address these two problems, in this paper, we employ inverse reinforcement learning (IRL) for text generation. Specifically, the IRL framework learns a reward function on training data, and then an optimal policy to maximum the expected total reward. Similar to the adversarial models, the reward and policy function in IRL are optimized alternately. Our method has two advantages: (1) the reward function can produce more dense reward signals. (2) the generation policy, trained by "entropy regularized" policy gradient, encourages to generate more diversified texts. Experiment results demonstrate that our proposed method can generate higher quality texts than the previous methods.


Clustering Meets Implicit Generative Models

arXiv.org Machine Learning

Clustering is a cornerstone of unsupervised learning which can be thought as disentangling multiple generative mechanisms underlying the data. In this paper we introduce an algorithmic framework to train mixtures of implicit generative models which we particularize for variational autoencoders. Relying on an additional set of discriminators, we propose a competitive procedure in which the models only need to approximate the portion of the data distribution from which they can produce realistic samples. As a byproduct, each model is simpler to train, and a clustering interpretation arises naturally from the partitioning of the training points among the models. We empirically show that our approach splits the training distribution in a reasonable way and increases the quality of the generated samples.