NEO performs test-time adaptation by re-centering target latent embeddings at the origin, boosting accuracy on distribution-shifted datasets like ImageNet-C with no optimization or hyperparameters and minimal extra compute.
The first way is that we use the accuracy achieved on samples used during the adaptation process
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
UNVERDICTED 1representative citing papers
citing papers explorer
-
NEO: No-Optimization Test-Time Adaptation through Latent Re-Centering
NEO performs test-time adaptation by re-centering target latent embeddings at the origin, boosting accuracy on distribution-shifted datasets like ImageNet-C with no optimization or hyperparameters and minimal extra compute.