Towards WARSHIP: Combining Components of Brain-Inspired Computing of RSH for Image Super Resolution
This work addresses the need for more robust and efficient deep learning methods in computer vision, though it appears incremental as it builds on existing components.
The paper tackles the problem of identifying durable deep learning components for vision by proposing WARSHIP, a brain-inspired framework, and demonstrates its application in image super-resolution with a CNN model that balances speed and performance.
Evolution of deep learning shows that some algorithmic tricks are more durable , while others are not. To the best of our knowledge, we firstly summarize 5 more durable and complete deep learning components for vision, that is, WARSHIP. Moreover, we give a biological overview of WARSHIP, emphasizing brain-inspired computing of WARSHIP. As a step towards WARSHIP, our case study of image super resolution combines 3 components of RSH to deploy a CNN model of WARSHIP-XZNet, which performs a happy medium between speed and performance.