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Unleashing the power of meta-tuning for few-shot generalization through sparse interpolated experts
S Chen, J Tack, Y Yang, YW Teh, JR Schwarz… - arxiv preprint arxiv …, 2024 - arxiv.org
Recent successes suggest that parameter-efficient fine-tuning of foundation models as the
state-of-the-art method for transfer learning in vision, replacing the rich literature of …
state-of-the-art method for transfer learning in vision, replacing the rich literature of …
Transductive active learning: Theory and applications
J Hübotter, B Sukhija, L Treven, Y As… - arxiv preprint arxiv …, 2024 - arxiv.org
We study a generalization of classical active learning to real-world settings with concrete
prediction targets where sampling is restricted to an accessible region of the domain, while …
prediction targets where sampling is restricted to an accessible region of the domain, while …
Meta-learning algorithms and applications
O Bohdal - 2024 - era.ed.ac.uk
Meta-learning in the broader context concerns how an agent learns about their own
learning, allowing them to improve their learning process. Learning how to learn is not only …
learning, allowing them to improve their learning process. Learning how to learn is not only …
Study on the application of transfer learning in small sample image classification of military equipment
L Lu, H Yu, H **ao, L Bao - Fifth International Conference on …, 2025 - spiedigitallibrary.org
To address the issue of insufficient military equipment sample data, which cannot meet the
training requirements of deep neural networks and tends to cause overfitting, this paper …
training requirements of deep neural networks and tends to cause overfitting, this paper …
[HTML][HTML] 基于迁移学**的军事少样本集成分类研究
鲁磊纪， 余红霞， 肖红菊， 鲍蕾 - Computer Science and Application, 2024 - hanspub.org
深度神经网络是一种需要大量的数据来进行有效训练的模型. 军事装备类数据普遍存在数据量较
少, 无法满足深度神经网络的训练需求, 容易出现过拟合的问题. 针对该问题 …
少, 无法满足深度神经网络的训练需求, 容易出现过拟合的问题. 针对该问题 …