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Found 2 papers, 1 papers with code
Rethinking Two Consensuses of the Transferability in Deep Learning
Deep transfer learning (DTL) has formed a long-term quest toward enabling deep neural networks (DNNs) to reuse historical experiences as efficiently as humans.
MetaLR: Meta-tuning of Learning Rates for Transfer Learning in Medical Imaging
In this work, we propose a meta-learning-based LR tuner, named MetaLR, to make different layers automatically co-adapt to downstream tasks based on their transferabilities across domains.
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