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Found 3 papers, 1 papers with code
SF(DA)$^2$: Source-free Domain Adaptation Through the Lens of Data Augmentation
We construct an augmentation graph in the feature space of the pretrained model using the neighbor relationships between target features and propose spectral neighborhood clustering to identify partitions in the prediction space.
Entropy is not Enough for Test-Time Adaptation: From the Perspective of Disentangled Factors
To mitigate it, TTA methods have utilized the model output's entropy as a confidence metric that aims to determine which samples have a lower likelihood of causing error.
Gradient Alignment with Prototype Feature for Fully Test-time Adaptation
In context of Test-time Adaptation(TTA), we propose a regularizer, dubbed Gradient Alignment with Prototype feature (GAP), which alleviates the inappropriate guidance from entropy minimization loss from misclassified pseudo label.
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