  Traditional CNN models are trained and tested on relatively low resolution
images (<300 px), and cannot be directly operated on large-scale images due to
compute and memory constraints. We propose Patch Gradient Descent (PatchGD), an
effective learning strategy that allows to train the existing CNN architectures
on large-scale images in an end-to-end manner. PatchGD is based on the
hypothesis that instead of performing gradient-based updates on an entire image
at once, it should be possible to achieve a good solution by performing model
updates on only small parts of the image at a time, ensuring that the majority
of it is covered over the course of iterations. PatchGD thus extensively enjoys
better memory and compute efficiency when training models on large scale
images. PatchGD is thoroughly evaluated on two datasets - PANDA and UltraMNIST
with ResNet50 and MobileNetV2 models under different memory constraints. Our
evaluation clearly shows that PatchGD is much more stable and efficient than
the standard gradient-descent method in handling large images, and especially
when the compute memory is limited.
