Posted by Eileen Mao and Tanjin Prity, Engineering Practicum Interns at Google, Summer 2019

The Cycle Generative adversarial Network, or CycleGAN for short, is a generator model for converting images from one domain to another domain. For example, the model can be used to translate images of horses to images of zebras, or photographs of city landscapes…

Posted by Debidatta Dwibedi, Research Associate, Google Research In the last few years there has been great progress in the field of video unders...

Since the release of PyTorch 1.0, we’ve seen the community expand to add new tools, contribute to a growing set of models available in the PyTorch Hub, and continually increase usage in both research and production.

We propose a neural network based approach for extracting models from dynamic
data using ordinary and partial differential equations. In particular, given a
time-series or spatio-temporal dataset,...

Misspelling Oblivious Embeddings (MOE) is a new model for word embeddings that are resilient to misspellings, improving the ability to apply word embeddings to real-world situations, where misspellings are common.

Progress on object detection is enabled by datasets that focus the research community's attention on open challenges. This process led us from simple images to complex scenes and from bounding...

Progressive Growing GAN is an extension to the GAN training process that allows for the stable training of generator models that can output large high-quality images.

It involves starting with a very small image and incrementally adding blocks of layers…

We present a corpus of Finnish news articles with a manually prepared named
entity annotation. The corpus consists of 953 articles (193,742 word tokens)
with six named entity classes...

We propose VisualBERT, a simple and flexible framework for modeling a broad range of vision-and-language tasks. VisualBERT consists of a stack of Transformer layers that implicitly align elements...

Recently, Facebook AI has advanced state-of-the-art results in key language understanding tasks and also launched a new benchmark to push AI systems further