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Winning lottery tickets at up to 95% sparsity can be harvested essentially for free simply by piggybacking iterative magnitude pruning onto the natural retraining loop of active learning.
Fixing the teacher's weights during Test-Time Adaptation can lead to substantial performance gains and greater robustness against hyperparameter changes.
Backpropagation-free test-time adaptation can be both accurate and efficient: PACE achieves state-of-the-art accuracy while slashing runtime by over 50%.
