From DeepMind: For almost 20 years, the StarCraft game series has been widely recognised as the pinnacle of 1v1 competitive video games, and among the best PC games of all time. The original StarCraft was an early pioneer in eSports, played at the highest level by elite professional players since the late 90s, and remains incredibly competitive to this day. The StarCraft series’ longevity in competitive gaming is a testament to Blizzard’s design, and their continual effort to balance and refine their games over the years. StarCraft II continues the series’ renowned eSports tradition, and has been the focus of our work with Blizzard.
DeepMind is on a scientific mission to push the boundaries of AI, developing programs that can learn to solve any complex problem without needing to be told how. Games are the perfect environment in which to do this, allowing us to develop and test smarter, more flexible AI algorithms quickly and efficiently, and also providing instant feedback on how we’re doing through scores... (more)
From Alex J. Champandard: As seen on TV! What if you could increase the resolution of your photos using technology from CSI laboratories? Thanks to deep learning and #NeuralEnhance, it's now possible to train a neural network to zoom in to your images at 2x or even 4x. You'll get even better results by increasing the number of neurons or training with a dataset similar to your low resolution image.
The catch? The neural network is hallucinating details based on its training from example images. It's not reconstructing your photo exactly as it would have been if it was HD. That's only possible in Hollywood — but using deep learning as "Creative AI" works and it is just as cool! (github)
From Manuel Ruder, Alexey Dosovitskiy, Thomas Brox of the University of Freiburg:
In the past, manually re-drawing an image in a certain artistic style required a professional artist and a long time. Doing this for a video sequence single-handed was beyond imagination. Nowadays computers provide new possibilities. We present an approach that transfers the style from one image (for example, a painting) to a whole video sequence. We make use of recent advances in style transfer in still images and propose new initializations and loss functions applicable to videos. This allows us to generate consistent and stable stylized video sequences, even in cases with large motion and strong occlusion. We show that the proposed method clearly outperforms simpler baselines both qualitatively and quantitatively... (pdf paper)
Records 1 to 4 of 4