  With the proliferation of deep learning methods, many computer vision
problems which were considered academic are now viable in the consumer setting.
One drawback of consumer applications is lossy compression, which is necessary
from an engineering standpoint to efficiently and cheaply store and transmit
user images. Despite this, there has been little study of the effect of
compression on deep neural networks and benchmark datasets are often losslessly
compressed or compressed at high quality. Here we present a unified study of
the effects of JPEG compression on a range of common tasks and datasets. We
show that there is a significant penalty on common performance metrics for high
compression. We test several methods for mitigating this penalty, including a
novel method based on artifact correction which requires no labels to train.
