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Accelerating the Training of Video Super-Resolution Models
[article]
2022
arXiv
pre-print
Despite that convolution neural networks (CNN) have recently demonstrated high-quality reconstruction for video super-resolution (VSR), efficiently training competitive VSR models remains a challenging problem. It usually takes an order of magnitude more time than training their counterpart image models, leading to long research cycles. Existing VSR methods typically train models with fixed spatial and temporal sizes from beginning to end. The fixed sizes are usually set to large values for
arXiv:2205.05069v2
fatcat:gqnj2mfjdrdapbcqjwtylvvpd4