Transfer-learning methods aim to improve performance in a data-scarce target domain using
a model pretrained on a data-rich source domain. A cost-efficient strategy, linear probing …

Transfer learning aims to leverage models pre-trained on source data to efficiently adapt to
target setting, where only limited data are available for model fine-tuning. Recent works …

Transfer of pre-trained representations improves sample efficiency and simplifies
hyperparameter tuning when training deep neural networks for vision. We revisit the …

Transfer learning has attracted a lot of attention in the past decade. One crucial research
issue in transfer learning is how to find a good representation for instances of different …

Transfer learning has gained a lot of attention and interest in the past decade. One crucial
research issue in transfer learning is how to find a good representation for instances of …

The increasing of pre-trained models has significantly facilitated the performance on limited
data tasks with transfer learning. However, progress on transfer learning mainly focuses on …

Transfer learning aims at improving the performance of target learners on target domains by
transferring the knowledge contained in different but related source domains. In this way, the …

Transfer learning is beneficial by allowing the expressive features of models pretrained on
large-scale datasets to be finetuned for the target task of smaller, more domain-specific …

Transfer of pre-trained representations improves sample efficiency and simplifies
hyperparameter tuning when training deep neural networks for vision. We revisit the …

We propose a new principle for transfer learning, based on a straightforward intuition: if two
domains are similar to each other, the model trained on one domain should also perform …