Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! You can even style videos! It takes 100ms on a 2015 Titan X to style the MIT Stata Center (1024×680) like Udnie, by Francis Picabia. Our implementation is based off of a combination of Gatys' A Neural Algorithm of Artistic Style , Johnson's Perceptual Losses for Real-Time Style Transfer and Super-Resolution , and Ulyanov's Instance Normalization . Running on FloydHub It is very easy to train, evaluate and serve fast style transfer on [Floydhub][ https://www.floydhub.com/ ]. Follow the instructions below: Visit Floydhub site to create an account if you do not have one. Clone this repository to your machine. Run floyd init <project name> inside the directory. Now you can: Train a new model Evaluate an existing model Serve a trained model at a public API. Video Stylization Here we transformed every frame in a video, then co
Awesome-AutoML-Papers A curated list of automated machine learning papers, articles, tutorials, slides and projects. Introduction to AutoML Machine learning (ML) has achieved considerable successes in recent years and an ever-growing number of disciplines rely on it. However, this success crucially relies on human machine learning experts to perform the following tasks: Preprocess the data Select appropriate features Select an appropriate model family Optimize model hyperparameters Postprocess machine learning models Critically analyze the results obtained. As the complexity of these tasks is often beyond non-ML-experts, the rapid growth of machine learning applications has created a demand for off-the-shelf machine learning methods that can be used easily and without expert knowledge. We call the resulting research area that targets progressive automation of machine learning AutoML . AutoML draws on many disciplines of machine learning, prominently including Bay