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University of Cambridge
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Inertial activity recognition models can now learn rich, global motion structure from unlabeled data by discovering and grouping motion primitives, achieving state-of-the-art performance with orders of magnitude less data.
Compressed models may achieve high accuracy but suffer a dramatic drop in adaptability during test-time, revealing a critical trade-off in model deployment.
