{"schema_version":"onlylabs.public_signal.v1","title":"Scaleway Writing: Introducing GPU Instances: Using Deep Learning to Obtain Frontal Rendering of Facial Images","description":"Scaleway writing signal with public source context, captured evidence pages, related signals, and category-scoped analysis context.","url":"https://onlylabs.fyi/signals/5bcf5276-a9d0-4f4c-a190-86d1176b8101","json_url":"https://onlylabs.fyi/signals/5bcf5276-a9d0-4f4c-a190-86d1176b8101/signal.json","generated_at":"2026-06-08T15:46:57.572+00:00","org":{"slug":"scaleway","name":"Scaleway","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/scaleway","dossier_json_url":"https://onlylabs.fyi/labs/scaleway/dossier.json"},"related_urls":{"signal":"https://onlylabs.fyi/signals/5bcf5276-a9d0-4f4c-a190-86d1176b8101","signal_json":"https://onlylabs.fyi/signals/5bcf5276-a9d0-4f4c-a190-86d1176b8101/signal.json","source":"https://www.scaleway.com/en/blog/gpu-instances-using-deep-learning-to-obtain-frontal-rendering-of-facial-images/","lab_dossier":"https://onlylabs.fyi/labs/scaleway","lab_dossier_json":"https://onlylabs.fyi/labs/scaleway/dossier.json","analysis":"https://onlylabs.fyi/analysis/scaleway","analysis_json":"https://onlylabs.fyi/analysis/scaleway/analysis.json","analysis_evidence_json":"https://onlylabs.fyi/analysis/scaleway/evidence.json","category":"https://onlylabs.fyi/neoclouds","category_json":"https://onlylabs.fyi/neoclouds.json","category_feed":"https://onlylabs.fyi/neoclouds/feed.xml","category_signals_json":"https://onlylabs.fyi/signals.json?category=neocloud","topic":"https://onlylabs.fyi/topics/talking","topic_signals_json":"https://onlylabs.fyi/topics/talking/signals.json?category=neocloud","topic_feed":"https://onlylabs.fyi/topics/talking/feed.xml?category=neocloud","data_business":null},"answer_pack":{"answer":"Scaleway published Introducing GPU Instances: Using Deep Learning to Obtain Frontal Rendering of Facial Images. 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Powered by high-end 16-GB NVIDIA Tesla P100 cards and highly efficient Intel Xeon Gold 6148 CPUs, they are ideal for data processing, artificial intelligence, rendering, and video encoding. In addition to the dedicated GPU and 10 Intel Xeon Gold cores, each instance comes with 45 GB of memory, 400 GB of local NVMe SSD storage, and is billed €1 per hour or €500 per month. Today, we present you with a concrete use case for GPU Instances using deep learning to obtain a frontal rendering of facial images. Feel free to try it too. To do so, visit the Scaleway console to ask for quotas before creating your first GPU Instance. GPU Overview Graphical processing unit (GPU) became a go-to term for the specialized electronic circuit designed to power graphics on a machine in the late 1990s, when it was popularized by the chip manufacturer NVIDIA. GPUs were originally produced primarily to drive high-quality gaming experiences, producing life-like digital..."},"evidence_pages":[{"url":"https://arxiv.org/pdf/1406.2661.pdf","final_url":"https://arxiv.org/pdf/1406.2661","title":"Introducing GPU Instances: Using Deep Learning to Obtain Frontal Rendering of Facial Images","http_status":200,"content_type":"application/pdf","capture_method":"exa","fetched_at":"2026-06-08T15:46:57.572+00:00","bytes":530482,"raw_path":"ee65f498ce3f3e038de21a5eed436db665235814bd306efa9041bbaf4bdd3ce7.pdf","content_hash":"ff5819e3a7b713c3bd3107b7de3d51fe0a347aa5d8444f0efdcf2345ef0a8b63","excerpt_chars":1200,"truncated":true,"excerpt":"[1406.2661] Generative Adversarial Nets Generative Adversarial Nets Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio Département d’informatique et de recherche opérationnelle Université de Montréal Montréal, QC H3C 3J7 Jean Pouget-Abadie is visiting Université de Montréal from Ecole Polytechnique. Sherjil Ozair is visiting Université de Montréal from Indian Institute of Technology DelhiYoshua Bengio is a CIFAR Senior Fellow. Abstract We propose a new framework for estimating generative models via an adversarial process, in which we simultaneously train two models: a generative model $G$ that captures the data distribution, and a discriminative model $D$ that estimates the probability that a sample came from the training data rather than $G$ . The training procedure for $G$ is to maximize the probability of $D$ making a mistake. This framework corresponds to a minimax two-player game. In the space of arbitrary functions $G$ and $D$ , a unique solution exists, with $G$ recovering the training data distribution and $D$ equal to $\\frac{1}{2}$ everywhere. In the case where $G$ and $D$ are defined by..."},{"url":"https://www.scaleway.com/en/blog/gpu-instances-using-deep-learning-to-obtain-frontal-rendering-of-facial-images/","final_url":"https://www.scaleway.com/en/blog/gpu-instances-using-deep-learning-to-obtain-frontal-rendering-of-facial-images/","title":"Introducing GPU Instances: Using Deep Learning to Obtain Frontal Rendering of Facial Images","http_status":200,"content_type":"text/html; charset=utf-8","capture_method":"plain","fetched_at":"2026-06-07T21:19:29.024711+00:00","bytes":283959,"raw_path":"98e4e3e0e34dd211eb460fe9357f553518b17516e7011b083cc852ca06f3de71.html","content_hash":"629fd4b8f9d371323007e84513bedfd89db67bcba5eba2723adc8c8ee99e779c","excerpt_chars":1200,"truncated":true,"excerpt":"Introducing GPU Instances: Using Deep Learning to Obtain Frontal Rendering of Facial Images Build • Olga Petrova • 21/07/20 • 10 min read We just released GPU Instances , our first servers equipped with graphical processing units (GPUs). 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