Effective and Efficient Data Poisoning in Semi-Supervised Learning

Adriano Franci
Adriano Franci
Martin Gubri
Martin Gubri
Cited by: 0|Views11

Abstract:

Semi-Supervised Learning (SSL) aims to maximize the benefits of learning from a limited amount of labelled data together with a vast amount of unlabelled data. Because they rely on the known labels to infer the unknown labels, SSL algorithms are sensitive to data quality. This makes it important to study the potential threats related to...More

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