Add to ORKGWe present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains both generation and inference, and has the quality of conditional and unconditional samples boosted by adversarial learning. Unlike previous hybrids of autoencoders and adversarial networks, the adversarial game in our approach is set up directly between the encoder and the generator, and no external mappings are trained in the process of learning. The game objective compares the divergences of each of the real and the generated data distributions with the prior distribution in the latent space. We show that direct generator-vs-encoder game leads to a tight coupling of the two components, resulting in samples and reconstructions of a comparabl...
We consider the problem of learning deep gener-ative models from data. We formulate a method that ge...
Recent generative adversarial networks (GANs) are able to generate impressive photo-realistic images...
Generative adversarial networks (GAN) have received much attention lately for its use with images an...
We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains b...
Allowing effective inference of latent vectors while training GANs can greatly increase their applic...
We introduce a novel generative autoencoder network model that learns to encode and reconstruct imag...
The Generative Adversarial Network framework has shown success in implicitly modeling data distribut...
We present a new adversarial learning method for deep reinforcement learning (DRL). Based on this me...
Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts o...
Generative adversarial networks are currently used to solve various problems and are one of the most...
Generative Adversarial Networks (GANs) are recently invented generative models which can produce hig...
© 1997-2012 IEEE. Generative adversarial networks (GANs) have been effective for learning generative...
© 1989-2012 IEEE. We develop an adversarial learning algorithm for supervised classification in gene...
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and p...
Generative Adversarial Neural Networks are neural networks which participate in a zero- sum game, co...
We consider the problem of learning deep gener-ative models from data. We formulate a method that ge...
Recent generative adversarial networks (GANs) are able to generate impressive photo-realistic images...
Generative adversarial networks (GAN) have received much attention lately for its use with images an...
We present a new autoencoder-type architecture that is trainable in an unsupervised mode, sustains b...
Allowing effective inference of latent vectors while training GANs can greatly increase their applic...
We introduce a novel generative autoencoder network model that learns to encode and reconstruct imag...
The Generative Adversarial Network framework has shown success in implicitly modeling data distribut...
We present a new adversarial learning method for deep reinforcement learning (DRL). Based on this me...
Unsupervised learning is of growing interest because it unlocks the potential held in vast amounts o...
Generative adversarial networks are currently used to solve various problems and are one of the most...
Generative Adversarial Networks (GANs) are recently invented generative models which can produce hig...
© 1997-2012 IEEE. Generative adversarial networks (GANs) have been effective for learning generative...
© 1989-2012 IEEE. We develop an adversarial learning algorithm for supervised classification in gene...
Generative Adversarial Networks (GANs) can successfully approximate a probability distribution and p...
Generative Adversarial Neural Networks are neural networks which participate in a zero- sum game, co...
We consider the problem of learning deep gener-ative models from data. We formulate a method that ge...
Recent generative adversarial networks (GANs) are able to generate impressive photo-realistic images...
Generative adversarial networks (GAN) have received much attention lately for its use with images an...