IMSE adapts Vision Transformers for test-time and continual test-time adaptation by tuning only singular values from SVD decompositions and using expert diversity plus domain retrieval, reaching SOTA with far fewer trainable parameters.
Becotta: Input-dependent online blending of experts for continual test-time adaptation
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
fields
cs.CV 3years
2026 3verdicts
UNVERDICTED 3representative citing papers
DO-ALL applies dataset distillation to generate synthetic source anchors that stabilize continual test-time adaptation under evolving domains without storing original source data.
MoASE++ combines activation sparsity experts with domain-adaptive on-policy distillation to achieve state-of-the-art continual test-time adaptation on image classification and segmentation benchmarks.
citing papers explorer
-
IMSE: Intrinsic Mixture of Spectral Experts Fine-tuning for Test-Time Adaptation
IMSE adapts Vision Transformers for test-time and continual test-time adaptation by tuning only singular values from SVD decompositions and using expert diversity plus domain retrieval, reaching SOTA with far fewer trainable parameters.
-
Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation
DO-ALL applies dataset distillation to generate synthetic source anchors that stabilize continual test-time adaptation under evolving domains without storing original source data.
-
MoASE++: Mixture of Activation Sparsity Experts with Domain-Adaptive On-policy Distillation for Continual Test Time Adaptation
MoASE++ combines activation sparsity experts with domain-adaptive on-policy distillation to achieve state-of-the-art continual test-time adaptation on image classification and segmentation benchmarks.