PFB prunes training samples based on the probability density of their shallow-layer features, blocking pruned samples from deep-layer forward and backward passes, and reports lossless or better accuracy with up to 33% time savings.
Fast kernel classifiers with online and active learn- ing
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Partial Forward Blocking: A Novel Data Pruning Paradigm for Lossless Training Acceleration
PFB prunes training samples based on the probability density of their shallow-layer features, blocking pruned samples from deep-layer forward and backward passes, and reports lossless or better accuracy with up to 33% time savings.