A federated-learning FPS distribution predictor using per-player and per-game learnable kernels achieves a Wasserstein distance of 0.469, but the evaluation may leak identity information.
Deep Learning Techniques for Super-Resolution in Video Games
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abstract
The computational cost of video game graphics is increasing and hardware for processing graphics is struggling to keep up. This means that computer scientists need to develop creative new ways to improve the performance of graphical processing hardware. Deep learning techniques for video super-resolution can enable video games to have high quality graphics whilst offsetting much of the computational cost. These emerging technologies allow consumers to have improved performance and enjoyment from video games and have the potential to become standard within the game development industry.
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Predicting Quality of Video Gaming Experience Using Global-Scale Telemetry Data and Federated Learning
A federated-learning FPS distribution predictor using per-player and per-game learnable kernels achieves a Wasserstein distance of 0.469, but the evaluation may leak identity information.