Yet another post about generative adversarial networks (GANs), pix2pix and CycleGAN. You can already find lots of webs with great introductions to GANs (such as

Many people use prediction and inference synonymously although there is a subtle difference. Learn what it is here!

In this article, we release a part of our thesis, developed at Harvard, and submitted in May 2019. This piece is one building block of a larger body of work, investigating AI’s inception in…

pixelCNN - Google Search

generative image modeling - Google Search

vae autoencoder artwork - Google Search

3d voxel latent space - Google Search

full latent space - Google Search

full latent space - Google Search

full latent space - Google Search

The portrait was offered by Christie’s for sale in New York from Oct 23 to 25 was created with AI algorithm called GAN’s(Generative Adversarial Networks) by the Paris-based collective Obvious, whose…

sampling from latent space faces - Google Search

In this thesis, we explore the use of deep neural networks for generation of natural language. Specifically, we implement two sequence-to-sequence neural variational models - variational autoencoders (VAE) and variational encoder-decoders (VED). VAEs for text generation are difficult to train due to issues associated with the Kullback-Leibler (KL) divergence term of the loss function vanishing to zero. We successfully train VAEs by implementing optimization heuristics such as KL weight…

In this thesis, we explore the use of deep neural networks for generation of natural language. Specifically, we implement two sequence-to-sequence neural variational models - variational autoencoders (VAE) and variational encoder-decoders (VED). VAEs for text generation are difficult to train due to issues associated with the Kullback-Leibler (KL) divergence term of the loss function vanishing to zero. We successfully train VAEs by implementing optimization heuristics such as KL weight…