  Understanding if classifiers generalize to out-of-sample datasets is a
central problem in machine learning. Microscopy images provide a standardized
way to measure the generalization capacity of image classifiers, as we can
image the same classes of objects under increasingly divergent, but controlled
factors of variation. We created a public dataset of 132,209 images of mouse
cells, COOS-7 (Cells Out Of Sample 7-Class). COOS-7 provides a classification
setting where four test datasets have increasing degrees of covariate shift:
some images are random subsets of the training data, while others are from
experiments reproduced months later and imaged by different instruments. We
benchmarked a range of classification models using different representations,
including transferred neural network features, end-to-end classification with a
supervised deep CNN, and features from a self-supervised CNN. While most
classifiers perform well on test datasets similar to the training dataset, all
classifiers failed to generalize their performance to datasets with greater
covariate shifts. These baselines highlight the challenges of covariate shifts
in image data, and establish metrics for improving the generalization capacity
of image classifiers.

Machine Learning;Computer Vision and Pattern Recognition;Machine Learning