Accelerating the Training of Video Super-Resolution. (arXiv:2205.05069v1 [cs.CV])

Web: http://arxiv.org/abs/2205.05069

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 good performance, resulting to slow
training. However, is such a …

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