Streaming convolutional neural networks for end-to-end learning with multi-megapixel images

Understand

Due to memory constraints on current hardware, most convolution neural networks (CNN) are trained on sub-megapixel images.

• For example, most popular datasets in computer vision contain images much less than a megapixel in size (0.09MP for ImageNet and 0.001MP for CIFAR-10).
• In some domains such as medical imaging, multi-megapixel images are needed to identify the presence of disease accurately.
• We propose a novel method to directly train convolutional neural networks using any input image size end-to-end.