Introduces (ε,δ)-Recovery Complexity and (ε,ρ)-TTA Learnability with order-wise matching bounds on recovery time for TTA under gradual and abrupt shifts via a discrete surrogate model.
Beyond model adaptation at test time: A survey
9 Pith papers cite this work. Polarity classification is still indexing.
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Introduces Dynamic Style Bridging for forward-facilitation continual test-time adaptation by multi-level style injection on pre-generated class proxies to provide stable on-demand supervision under evolving distribution shifts.
An uncertainty-aware test-time adaptation framework improves cross-region spatio-temporal fusion of land surface temperature by updating only the fusion module guided by epistemic uncertainty, land use consistency, and bias correction.
Combines offline behavioral cloning with online Real-Time Recurrent RL fine-tuning on LrcSSM models to adapt autonomous driving policies to distribution shifts, validated in simulation and on a real 1:10-scale robot with event camera.
DOME learns sample-specific domain variables from sparse supervision via vision-language models and a sparse domain bank to improve test-time adaptation performance.
Entropy minimization amplifies prediction bias from merged feature clusters under distribution shifts, and DSBR mitigates collapse by equalizing predicted class contributions to the unsupervised loss.
Ramen enables robust test-time adaptation of vision-language models under mixed-domain shifts by actively selecting domain-consistent and prediction-balanced samples via an embedding-gradient cache.
Edge AI systems require ongoing adaptation to evolving data and constraints to avoid violating budgets or losing reliability, formalized via an Agent-System-Environment lens that defines ten future research challenges.
citing papers explorer
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On the Learnability of Test-Time Adaptation: A Recovery Complexity Perspective
Introduces (ε,δ)-Recovery Complexity and (ε,ρ)-TTA Learnability with order-wise matching bounds on recovery time for TTA under gradual and abrupt shifts via a discrete surrogate model.
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Dance Across Shifts: Forward-Facilitation Continual Test-Time Adaptation through Dynamic Style Bridging
Introduces Dynamic Style Bridging for forward-facilitation continual test-time adaptation by multi-level style injection on pre-generated class proxies to provide stable on-demand supervision under evolving distribution shifts.
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Uncertainty-Aware Test-Time Adaptation for Cross-Region Spatio-Temporal Fusion of Land Surface Temperature
An uncertainty-aware test-time adaptation framework improves cross-region spatio-temporal fusion of land surface temperature by updating only the fusion module guided by epistemic uncertainty, land use consistency, and bias correction.
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Adaptive Control in Autonomous Driving via Real-Time Recurrent RL
Combines offline behavioral cloning with online Real-Time Recurrent RL fine-tuning on LrcSSM models to adapt autonomous driving policies to distribution shifts, validated in simulation and on a real 1:10-scale robot with event camera.
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DOME: Learning Transferable Domain Variables from Sparse Supervision for Test-Time Adaptation
DOME learns sample-specific domain variables from sparse supervision via vision-language models and a sparse domain bank to improve test-time adaptation performance.
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Entropy Minimization without Model Collapse: Mitigating Prediction Bias in Medical Imaging
Entropy minimization amplifies prediction bias from merged feature clusters under distribution shifts, and DSBR mitigates collapse by equalizing predicted class contributions to the unsupervised loss.
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Ramen: Robust Test-Time Adaptation of Vision-Language Models with Active Sample Selection
Ramen enables robust test-time adaptation of vision-language models under mixed-domain shifts by actively selecting domain-consistent and prediction-balanced samples via an embedding-gradient cache.
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Position Paper: From Edge AI to Adaptive Edge AI
Edge AI systems require ongoing adaptation to evolving data and constraints to avoid violating budgets or losing reliability, formalized via an Agent-System-Environment lens that defines ten future research challenges.