tiled convolution

Tiled convolution is a technique used in deep learning for processing large images or volumes that are too big to fit in memory. The image or volume is divided into smaller tiles, and each tile is processed separately using convolutional neural networks (CNNs). The outputs of the CNNs for each tile are then combined to produce the final output for the entire image or volume. Tiled convolution is particularly useful for processing high-resolution images or volumes, where the size of the input can exceed the available memory of the computer. It also allows for parallel processing of multiple tiles, which can significantly speed up the computation time.