FINE-TUNING OF TRANSDUCTIVE FEW-SHOT LEARNING METHODS USING MARGIN-BASED UNCERTAINTY WEIGHTING AND PROBABILITY REGULARIZATION

Abstract: disclosed herein is a novel method for improving transductive fine-tuning for few-shot learning using margin-based uncertainty weighting and probability regularization. margin-based uncertainty is designed to assign low loss weights for wrongly predicted samples and high loss weights for the correct ones. probability regularization provides for the probability of each testing sample being adjusted by a scale vector, which quantifies the difference between the class marginal distribution and the uniform.

Inventor(s): MARIOS SAVVIDES, RAN TAO, HAO CHEN

CPC Classification: G06N20/00 (Machine learning)

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