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Class-Incremental Continual Learning for General Purpose Healthcare Models
In this work, we investigate the performance of continual learning models on four different medical imaging scenarios involving ten classification datasets from diverse modalities, clinical specialties, and hospitals.
Pareto-Secure Machine Learning (PSML): Fingerprinting and Securing Inference Serving Systems
Second, we counter the proposed attack with a noise-based defense mechanism that thwarts fingerprinting by adding noise to the specified performance metrics.
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