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arXiv · 2306.02090

Deep Classifier Mimicry without Data Access

Steven Braun·Martin Mundt·Kristian Kersting
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Abstract

Access to pre-trained models has recently emerged as a standard across numerous machine learning domains. Unfortunately, access to the original data the models were trained on may not equally be granted. This makes it tremendously challenging to fine-tune, compress models, adapt continually, or to do any other type of data-driven update. We posit that original data access may however not be required. Specifically, we propose Contrastive Abductive Knowledge Extraction (CAKE), a model-agnostic knowledge distillation procedure that mimics deep classifiers without access to the original data. To this end, CAKE generates pairs of noisy synthetic samples and diffuses them contrastively toward a model's decision boundary. We empirically corroborate CAKE's effectiveness using several benchmark datasets and various architectural choices, paving the way for broad application.

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Steven Braun, Martin Mundt, Kristian Kersting. 2023-06-03. Deep Classifier Mimicry without Data Access. https://arxiv.org/abs/2306.02090

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