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PALM: Pushing Adaptive Learning Rate Mechanisms for Continual Test-Time Adaptation
Real-world vision models in dynamic environments face rapid shifts in domain distributions, leading to decreased recognition performance.
LaplaceConfidence: a Graph-based Approach for Learning with Noisy Labels
We introduce LaplaceConfidence, a method that to obtain label confidence (i. e., clean probabilities) utilizing the Laplacian energy.
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