7 papers
Test-Time Distillation for Continual Model Adaptation
Xiao Chen, Jiazhen Huang, Zhiming Liu +4
Deep neural networks often suffer performance degradation upon deployment due to distribution shifts. Continual Test-Time Adaptation (CTTA) aims to address this issue in an unsuper…
Semantic-Space Exploration and Exploitation in RLVR for LLM Reasoning
Fanding Huang, Guanbo Huang, Xiao Fan +7
Reinforcement Learning with Verifiable Rewards (RLVR) for LLM reasoning is often framed as balancing exploration and exploitation in action space, typically operationalized with to…
Test-Time Adaptation for Tactile-Vision-Language Models
Chuyang Ye, Haoxian Jing, Qinting Jiang +4
Tactile-vision-language (TVL) models are increasingly deployed in real-world robotic and multimodal perception tasks, where test-time distribution shifts are unavoidable. Existing…
DATTA: Domain Diversity Aware Test-Time Adaptation for Dynamic Domain Shift Data Streams
Chuyang Ye, Dongyan Wei, Zhendong Liu +5
Test-Time Adaptation (TTA) addresses domain shifts between training and testing. However, existing methods assume a homogeneous target domain (e.g., single domain) at any given tim…
MoETTA: Test-Time Adaptation Under Mixed Distribution Shifts with MoE-LayerNorm
Xiao Fan, Jingyan Jiang, Zhaoru Chen +6
Test-Time adaptation (TTA) has proven effective in mitigating performance drops under single-domain distribution shifts by updating model parameters during inference. However, real…
Feature-Based Instance Neighbor Discovery: Advanced Stable Test-Time Adaptation in Dynamic World
Qinting Jiang, Chuyang Ye, Dongyan Wei +4
Despite progress, deep neural networks still suffer performance declines under distribution shifts between training and test domains, leading to a substantial decrease in Quality o…