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Lacking supervised data is an issue while training deep neural networks(DNNs), mainly when considering medical and biological data where supervisionis expensive. Recently, Embedded Pseudo-Labeling (EPL) addressed this problemby using a non-linear projection (t-SNE) from a feature space of the DNN to a2D space, followed by semi-supervised label propagation using aconnectivity-based method (OPFSemi). We argue that the performance of the finalclassifier depends on the data separation present in the latent space andvisual separation present in the projection. We address this by first proposingto use contrastive learning to produce the latent space for EPL by two methods(SimCLR and SupCon) and by their combination, and secondly by showing, via anextensive set of experiments, the aforementioned correlations between dataseparation, visual separation, and classifier performance. We demonstrate ourresults by the classification of five real-world challenging image datasets ofhuman intestinal parasites with only 1% supervised samples.

Linking data separation, visual separation, and classifier performance using pseudo-labeling by contrastive learning