How to protect contrastive learning models against adversarial attacks
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Contrastive learning (CL) is a machine learning technique that has gained popularity in the past few years because it reduces the need for annotated data, one of the main pain points of developing ML models. But due to its peculiarities, contrastive learning presents security challenges that are different from those found in supervised machine learning. Machine learning and security researchers are worried about the effect of adversarial attacks on ML models trained through contrastive learning.
How to protect contrastive learning models against adversarial attacks
How to protect contrastive learning models…
How to protect contrastive learning models against adversarial attacks
Contrastive learning (CL) is a machine learning technique that has gained popularity in the past few years because it reduces the need for annotated data, one of the main pain points of developing ML models. But due to its peculiarities, contrastive learning presents security challenges that are different from those found in supervised machine learning. Machine learning and security researchers are worried about the effect of adversarial attacks on ML models trained through contrastive learning.