Paper
Dados & Embeddings
Distill Once, Adapt Life-Long: Exploring Dataset Distillation for Continual Test-Time Adaptation
DO-ALL is a test-time adaptation framework that uses dataset distillation to create synthetic anchors for stable long-term model performance without retaining source data.
Hugging Face · Daily Papers
·Hyun-Kurl Jang, Jihun Kim
·
Este artigo está em destaque na seleção diária de papers do Hugging Face, curada pela comunidade de pesquisa em IA.
Autores: Hyun-Kurl Jang, Jihun Kim, Hyeokjun Kweon, Kuk-Jin Yoon
- 0 upvotes da comunidade
- Temas: Continual Test-Time Adaptation, Dataset Distillation, source-free adaptation, catastrophic forgetting, semantic alignment, source replay
Resumo
Resumo original (em inglês), extraído do paper:
DO-ALL is a test-time adaptation framework that uses dataset distillation to create synthetic anchors for stable long-term model performance without retaining source data.
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