Keywords: Image Reconstruction, Machine Learning/Artificial Intelligence, Self-supervised learningTo train a neural network to recover images with sub-sampled examples only, Self-Supervised Learning via Data Undersampling (SSDU) proposes partitioning the sampling mask into two subsets and training a network to recover one from the other. Recent work on the connection between SSDU and the self-supervised denoising method Noisier2Noise has shown that superior reconstruction quality is possible when the distribution of the partition matches the sampling mask. We test this principle on three types of sampling schemes and find that the test set loss is indeed closest to a fully supervised benchmark when the partition distribution is matched.
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