arXiv:2606. 26457v1 Announce Type: cross Abstract: This paper presents a probabilistic framework for online test-time adaptation problems.
By Daniel Corrales, David R\'ios Insua
arXiv:2606. 31420v1 Announce Type: new Abstract: Test-Time Adaptation (TTA) enables models trained on a source domain to adapt online to unlabeled test data under distribution shifts.
By Shaoyang Huang, Yashi Zhu, Yichen Yu, Lei Zhang, Zhang Yi, Tao He
arXiv:2607. 23735v1 Announce Type: new Abstract: In many real-world scenarios, encountering continual shifts in domain during inference is very common.
By Anurag Roy, Riddhiman Moulick, Vinay Kumar Verma, Saptarshi Ghosh, Abir Das
The paper introduces DGOTTA, a framework for temporal memory‑aware online test‑time adaptation on dynamic graphs. DGOTTA comprises three modules: temporal‑aware augmentation to diversify test graphs, memory‑aware model prediction to mitigate catastrophic forgetting, and consistency‑guided online adaptation to enforce temporal alignment and smoothness. Experiments on three real‑world datasets and four DGNN backbones show that DGOTTA improves generalization under diverse distribution shifts and across multiple model architectures.
By Bo Li, Xin Zheng, Ming Jin, Can Wang, Shirui Pan
This paper introduces TestDG, an online test-time domain generalization framework for continual test-time adaptation (CTTA). TestDG learns features invariant to both current and past test domains during testing, using a new model architecture, adaptation strategy, and prototype selection/update mechanisms. It achieves state‑of‑the‑art results on four CTTA benchmarks and demonstrates superior generalization to unseen test domains.
By Sohyun Lee, Nayeong Kim, Juwon Kang, Seong Joon Oh, Suha Kwak
The paper studies a two‑stage learning framework that first trains an offline model using approximate nonlinear‑least‑squares estimation and then adapts it online with a meta‑LMS algorithm to handle parameter drift in nonlinear stochastic dynamical systems. It provides an upper bound on the offline generalization error that accounts for strong data correlation and distribution shift via Kullback‑Leibler divergence, and it demonstrates that the combined offline‑online approach outperforms methods that rely solely on offline or online learning. Both theoretical analysis and empirical experiments support the claimed performance gains.
By Haizheng Li, Lei Guo