Few-Shot Unsupervised Image-to-Image Translation
Unsupervised image-to-image translation methods learn to map images in a given class to an analogous image in a different class, drawing on unstructured (non-registered) datasets of images. While remarkably successful, current methods require access to many images in both source and destination classes at training time. We argue this greatly limits their use. Drawing inspiration from the human cap
NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch | NVIDIA Developer Blog
NVIDIA Apex: Tools for Easy Mixed-Precision Training in PyTorch Most deep learning frameworks, including PyTorch, train using 32-bit floating point (FP32) arithmetic by default. However, using FP32 for all operations is not essential to achieve full accuracy for many state-of-the-art deep neural networks (DNNs). In 2017, NVIDIA researchers developed a methodology for mixed-precision training in wh
Transforming Standard Video Into Slow Motion with AI - NVIDIA Developer News CenterNVIDIA Developer News Center
Researchers from NVIDIA developed a deep learning-based system that can produce high-quality slow-motion videos from a 30-frame-per-second video, outperforming various state-of-the-art methods that aim to do the same. The researchers will present their work at the annual Computer Vision and Pattern Recognition (CVPR) conference in Salt Lake City, Utah this week. “There are many memorable moments
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GitHub - NVlabs/SPADE: Semantic Image Synthesis with SPADE
NVIDIA、30fpsの動画から高品質スローモーションが作り出せるAI技術
