3 citations · 3 across the 6 of their papers we have counts for
4 papers · 1 filter
Adapt in the Wild: Test-Time Entropy Minimization with Sharpness and Feature Regularization
Shuaicheng Niu, Guohao Chen, Deyu Chen +7
Test-time adaptation (TTA) may fail to improve or even harm the model performance when test data have: 1) mixed distribution shifts, 2) small batch sizes, 3) online imbalanced labe…
TS-LIF: A Temporal Segment Spiking Neuron Network for Time Series Forecasting
Shibo Feng, Wanjin Feng, Xingyu Gao +2
Spiking Neural Networks (SNNs) offer a promising, biologically inspired approach for processing spatiotemporal data, particularly for time series forecasting. However, conventional…
Test-Time Model Adaptation with Only Forward Passes
Shuaicheng Niu, Chunyan Miao, Guohao Chen +2
Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. However, in real-world scenarios, models are…
Uncertainty-Calibrated Test-Time Model Adaptation without Forgetting
Mingkui Tan, Guohao Chen, Jiaxiang Wu +4
Test-time adaptation (TTA) seeks to tackle potential distribution shifts between training and test data by adapting a given model w.r.t. any test sample. Although recent TTA has sh…