Deep learning has been shown to achieve impressive results in several domains
like computer vision and natural language processing. A key element of this
success has been the development of new...

A curated list of awesome open source libraries to deploy, monitor, version and scale your machine learning - EthicalML/awesome-production-machine-learning

Objects play a central role in computer vision and, increasingly, machine learning research.With many applications depending on object detection in images an...

In this tutorial, we will give you some deeper insights into recent developments in the field of Deep Learning NLP. The first part of the workshop will be an introduction into the dynamic deep learning library PyTorch. We will explain the key steps for building…

Dynamic magnetic resonance imaging (MRI) exhibits high correlations in
k-space and time. In order to accelerate the dynamic MR imaging and to exploit
k-t correlations from highly undersampled...

With roads in developed countries like the US changing up to 15% annually, Mapillary addresses a growing demand for keeping maps updated by combining images from any camera into a 3D visualization of the world. Mapillary’s independent and collaborative approach…

Plotly.py is the library that powers graphs and maps for Dash. Version 4.0 of Plotly.py (also known as the plotlymodule) is now available…

Semi-supervised learning is the challenging problem of training a classifier in a dataset that contains a small number of labeled examples and a much larger number of unlabeled examples. The Generative Adversarial Network, or GAN, is an architecture that…

A guest post by Kyle A. Pearson

The Least Squares Generative Adversarial Network, or LSGAN for short, is an extension to the GAN architecture that addresses the problem of vanishing gradients and loss saturation. It is motivated by the desire to provide a signal to the generator about fake…

We introduce MultiDepth, a novel training strategy and convolutional neural
network (CNN) architecture that allows approaching single-image depth
estimation (SIDE) as a multi-task problem. SIDE is...

In the last few years there have been important advancements in generative models with the two dominant approaches being Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs)....

Semantic segmentation refers to the process of linking each pixel in an image to a class label. These labels could include a person, car, flower, piece of furniture, etc., just to mention a few. We can think of semantic segmentation… Continue reading A 2019…

Image-to-image translation is the controlled conversion of a given source image to a target image. An example might be the conversion of black and white photographs to color photographs. Image-to-image translation is a challenging problem and often requires…

This repository reproduces the results of the paper: "Fixing the train-test resolution discrepancy" https://arxiv.org/abs/1906.06423 - facebookresearch/FixRes