Feature Extraction Framework based on Contrastive Learning with Adaptive Positive and Negative Samples. (arXiv:2201.03942v1 [cs.LG])

In this study, we propose a feature extraction framework based on contrastive
learning with adaptive positive and negative samples (CL-FEFA) that is suitable
for unsupervised, supervised, and semi-supervised single-view feature
extraction. CL-FEFA constructs adaptively the positive and negative samples
from the results of feature extraction, which makes it more appropriate and
accurate. Thereafter, the discriminative features are re extracted to according
to InfoNCE loss based on previous positive and negative samples, which will
make the intra-class samples more compact and …

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