The paper introduces DBCM, a Dual-Branch Calibration Module designed for test-time adaptation in time series forecasting. It addresses non‑stationarity by sharing adaptation across channels via a shared adapter, while adding a low‑rank channel‑specific adapter and channel mixing with gated modulation. Experiments demonstrate that DBCM outperforms existing methods across various datasets, horizons, and backbone models.
By Jaebyeong Jeon, Kunwoo Kang, Sang Yup Lee, Kibok Lee
arXiv:2609.24111v1 Announce Type: new
Abstract: Test-time adaptation (TTA) addresses distribution shift using only unlabeled test data. Existing methods typically adapt pretrained models by updating...
By Muhammad Sudipto Siam Dip, Ali Etemad
Open-Set Test-Time Adaptation (OSTTA) operates on non-stationary target streams where covariate and semantic shifts coexist. Existing TTA methods often obtain adaptation signals by updating model para...
arXiv:2603.28678v2 Announce Type: replace
Abstract: We introduce PACE, a backpropagation-free continual test-time adaptation system that directly optimizes the affine parameters of normalization laye...
By Damian S\'ojka, Sebastian Cygert, Marc Masana
Tail Cues, Principal Corrections: Plug-and-Play Rectification for Open-Set Test-Time Adaptation introduces TALOR, a lightweight module that rectifies features for non-stationary target streams by decomposing them into high-gain principal and low-gain tail coordinates. TALOR applies a soft-routed affine correction in the principal subspace, subtracts drift via weighted ridge regression, and reconstructs the feature for the source head. On ImageNet-C with six csOOD datasets, TALOR surpasses the next-best UniEnt by 2.2% H-score, runs 2.5× faster, and uses only 12% of its GPU memory, while also boosting COME to 68.4% H-score with minimal memory overhead.
By Yingkai Yang, Chaoqi Chen, Hui Huang
arXiv:2603.14254v2 Announce Type: replace
Abstract: Test-time adaptation (TTA) aims to improve model robustness under distribution shifts by adapting to unlabeled test data, but most existing methods...
By Ronghao Zhang, Shuaicheng Niu, Qi Deng, Yanjie Dong, Jian Chen, Runhao Zeng