Dec 13, 2023 · This study proposed the Feature Adaptive Distillation (FAD) method. Specifically, we capture broader variations in feature distributions through a novel ...
Dec 13, 2023 · To address the optimization problem for cross-domain few-shot image identification, this study proposed the Feature Adaptive Distillation (FAD) ...
Jan 25, 2024 · This paper proposes an adaptive transformer network (ADAPTER), a simple but effective solution for cross-domain few-shot learning where there ...
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Mar 15, 2024 · This paper proposes an adaptive transformer network (ADAPTER), a simple but effective solution for cross-domain few-shot learning.
Mar 24, 2024 · Cross-domain few-shot learning presents a formidable challenge, as models must be trained on base classes and then tested on novel classes from ...
This method effectively transfers knowledge from the source domain to the target domain by applying self-distillation techniques and mixed data augmentation.
This paper empirically investigates which pre-training is preferred based on domain similarity and few-shot difficulty of the target domain.
Jan 26, 2024 · This paper pro- poses an adaptive transformer network (ADAPTER), a simple but effective so- lution for cross-domain few-shot learning where ...
... Cross-domain few-shot learning based on feature adaptive distillation | Recently, few-shot learning (FSL) has exhibited remarkable performance in computer ...
Second, to address the pitfalls of noisy statistics, we deploy two strategies: a progressive training of the two adapters and an adaptive distillation technique ...