Sep 28, 2018 · newsletter

Realistic Video Generation

Generative Adversarial Networks (GANs) wowed the world in 2014 with their ability to generate what we considered to be realistic images. While these images were quite low resolution, researchers kept working on how to perfect these methods in order to increase the quality of the images and even to apply the algorithm on other types of data like text and sound.

However, until recently there has been little success in making realistic videos. The main problem with making videos is temporal consistency: while people can be forgiving in one frame and find some interpretation for unrealistic regions, we are adept at seeing inconsistencies with how videos progress.

For example, we can accept some strange looking texture in the background of an image as simply some strange looking background. However, if that background is randomly changing from frame to frame in a video, we immediately discount the video. It is exactly this temporal consistency which has plagued researchers trying to apply GANs to videos – while each frame seemed realistic taken on its own, when assembled into a video, there were considerable inconsistencies which ruined any illusion of realism. This restricted the ability to reuse models that showed success at generating individual images, and forced researchers to come up with new methods to deal with the temporal nature of videos.

Recently, researchers at NVIDIA and MIT have come up with a new type of GAN, vid2vid, which primarily addresses this problem by explicitly incorporating how things seem to be moving within the video, in order to continue this motion in future frames. (In addition, they follow previous work, which uses a multi-resolution approach for generating high resolution images). This is done by calculating the optical flow of the image, which is a classic computer vision method that simply has not been incorporated into such a model until now.

The results are quite staggering (we highly recommend watching their release video). With the model you can create dashboard camera footage from the initial segmentation frame (allowing you to change the type and shape of objects in the frame by simply drawing in the corresponding color); it’s even possible to create realistic looking dance videos from pose information. It’s interesting to see this new method as compared with previous methods, to really get a sense of how important this additional temporal information is for making realistic results.

These high quality results are quite exciting and are groundbreaking work in the field of video generation. From applications in generating synthetic training data to use in creative projects, the vid2vid model itself is instantly applicable.

Even more interesting is how the field as a whole will learn from this research and start finding ways to incorporate other classic algorithms into neural networks. Just as conv-nets explicitly encoded the two dimensional understanding we have for images into models so that they can more quickly and accurately learn how to work with that data, this method explicitly encodes our understanding of how frames of a video flow from one to another (albeit this was much trickier to do than the conv-net example!). We’re interested in seeing what other algorithms will be incorporated into neural networks like this and what capabilities these models will have.

Read more

Sep 28, 2018 · newsletter
Sep 28, 2018 · newsletter

Latest posts

Jun 22, 2020 · post

How to Explain HuggingFace BERT for Question Answering NLP Models with TF 2.0

by Victor · Figure 1: In this sample, a BERTbase model gets the answer correct (Achaemenid Persia). Model gradients show that the token “subordinate ..” is impactful in the selection of an answer to the question “Macedonia was under the rule of which country?". This makes sense .. good for BERTbase. Recently, our team at Fast Forward Labs have been exploring state of the art models for Question Answering and have used the rather excellent HuggingFace transformers library. more
Jun 16, 2020 · notebook

Evaluating QA: Metrics, Predictions, and the Null Response →

by Melanie · A deep dive into computing QA predictions and when to tell BERT to zip it! In our last post, Building a QA System with BERT on Wikipedia, we used the HuggingFace framework to train BERT on the SQuAD2.0 dataset and built a simple QA system on top of the Wikipedia search engine. This time, we’ll look at how to assess the quality of a BERT-like model for Question Answering.
May 19, 2020 · notebook

Building a QA System with BERT on Wikipedia →

by Melanie · So you’ve decided to build a QA system. You want to start with something simple and general so you plan to make it open domain using Wikipedia as a corpus for answering questions. You want to use the best NLP that your compute resources allow (you’re lucky enough to have access to a GPU) so you’re going to focus on the big, flashy Transformer models that are all the rage these days.
Apr 28, 2020 · notebook

Intro to Automated Question Answering →

by Melanie · Welcome to the first edition of the Cloudera Fast Forward blog on Natural Language Processing for Question Answering! Throughout this series, we’ll build a Question Answering (QA) system with off-the-shelf algorithms and libraries and blog about our process and what we find along the way. We hope to wind up with a beginning-to-end documentary that provides:
Apr 1, 2020 · newsletter

Enterprise Grade ML

by Shioulin · At Cloudera Fast Forward, one of the mechanisms we use to tightly couple machine learning research with application is through application development projects for both internal and external clients. The problems we tackle in these projects are wide ranging and cut across various industries; the end goal is a production system that translates data into business impact. What is Enterprise Grade Machine Learning? Enterprise grade ML, a term mentioned in a paper put forth by Microsoft, refers to ML applications where there is a high level of scrutiny for data handling, model fairness, user privacy, and debuggability. more
Apr 1, 2020 · post

Bias in Knowledge Graphs - Part 1

by Keita · Introduction This is the first part of a series to review Bias in Knowledge Graphs (KG). We aim to describe methods of identifying bias, measuring its impact, and mitigating that impact. For this part, we’ll give a broad overview of this topic. image credit: Mediamodifier from Pixabay Motivation Knowledge graphs, graphs with built-in ontologies, create unique opportunities for data analytics, machine learning, and data mining. They do this by enhancing data with the power of connections and human knowledge. more

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Cloudera Fast Forward is an applied machine learning reseach group.
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