Core Concepts
AUTOFT is a data-driven approach that significantly improves generalization to out-of-distribution inputs, surpassing existing methods.
Abstract
Foundation models encode rich representations adaptable to downstream tasks by fine-tuning.
AUTOFT proposes a data-driven approach for robust fine-tuning, enhancing out-of-distribution generalization.
Bi-level optimization is used to search for an objective function and hyperparameters.
AUTOFT achieves state-of-the-art performance on WILDS iWildCam and FMoW benchmarks.
The method is computationally inexpensive, requiring minimal additional compute compared to standard fine-tuning.
AUTOFT consistently outperforms existing methods in OOD metrics across various distribution shifts.
The learned objective is task-specific and not universal.
The choice of validation set and hyperparameter optimization algorithm significantly impacts performance.
AUTOFT improves OOD generalization with limited data in few-shot classification tasks.
The method is competitive with standard transfer learning methods on various datasets.
Stats
AUTOFT는 기존 방법을 훌륭하게 능가하는 OOD 입력에 대한 일반화를 크게 향상시키는 데이터 주도 접근 방식입니다.
AUTOFT는 WILDS iWildCam 및 FMoW 벤치마크에서 최첨단 성능을 달성합니다.
Quotes
"AUTOFT는 OOD 입력에 대한 일반화를 크게 향상시키는 데이터 주도 접근 방식입니다."
"AUTOFT는 WILDS iWildCam 및 FMoW 벤치마크에서 최첨단 성능을 달성합니다."