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We analyze a dataset of retinal images using linear probes: linear regressionmodels trained on some "target" task, using embeddings from a deepconvolutional (CNN) model trained on some "source" task as input. We use thismethod across all possible pairings of 93 tasks in the UK Biobank dataset ofretinal images, leading to ~164k different models. We analyze the performanceof these linear probes by source and target task and by layer depth. We observethat representations from the middle layers of the network are moregeneralizable. We find that some target tasks are easily predicted irrespectiveof the source task, and that some other target tasks are more accuratelypredicted from correlated source tasks than from embeddings trained on the sametask.

Using a Cross-Task Grid of Linear Probes to Interpret CNN Model Predictions On Retinal Images