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
The paper investigates how model compression impacts test-time adaptation (TTA) in deep neural networks. Using ResNet‑18 and ViT‑Base on CIFAR‑10‑C and ImageNet‑C, the authors evaluate several compression techniques alongside standard TTA methods, introducing a diagnostic framework to assess representational expressivity and adaptation subspace compatibility. Results show that while compressed models maintain high accuracy under supervised adaptation, their TTA performance deteriorates with increased compression due to reduced representational diversity and structural constraints that limit recoverability.
By Francesco Corti, Dong Wang, Young D. Kwon, Cecilia Mascolo, Olga Saukh
The paper introduces Quantizer‑Aligned Recalibration (QuAR), a single‑pass test‑time adaptation technique for quantized vision transformers that does not require backpropagation or parameter updates. QuAR recalibrates activations at the input of frozen quantizers by aligning per‑channel statistics with the source calibration, thereby correcting the distorted code distribution caused by distribution shift. On ImageNet‑C, QuAR outperforms state‑of‑the‑art backprop‑free methods across 3‑, 4‑, 6‑, and 8‑bit precisions, achieving higher accuracy, lower latency, and minimal memory overhead while maintaining performance across diverse shift scenarios.
By Hyeongheon Cha, Young D. Kwon, Sung-Ju Lee
arXiv:2604. 22005v2 Announce Type: replace-cross Abstract: Accurate yet low-latency channel state information (CSI) acquisition is essential for multiple-input multiple-output (MIMO) communication systems.
By Junjie Zhao, Guangming Liang, Xiaonan Liu, Dongzhu Liu
FORGE is a forward‑only test‑time adaptation technique designed for integer‑only vision models running on microcontrollers. It restores batch‑normalization statistics after BN folding by re‑normalizing each convolution’s per‑channel output using only forward‑pass estimates, enabling adaptation on deployed, folded integer models. The method achieves accuracy gains comparable to gradient‑based TENT, requires adapting only a few layers, works with single‑sample streaming, and has been validated on an ESP32‑S3 with minimal energy and latency overhead.
By Muhammad Rehan, Haider Ali, Muhammad Ali Munir, Moaz Amjad
arXiv:2505. 00986v3 Announce Type: replace Abstract: Continual Test-time adaptation (CTTA) continuously adapts the deployed model on every incoming batch of data.
By Xiao Ma, Young D. Kwon, Dong Ma
arXiv:2610.19700v1 Announce Type: new
Abstract: Low-rank adaptation (LoRA) enables parameter-efficient fine-tuning by learning low-rank weight updates. Increasing rank expands adaptation capacity, bu...
By Fanyao Li, Yuxi Liu, Xiaohao Liu, He Chang, Zhaoxin Wang, Zhulin Tao