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Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in com- puter vision. The typical way of conducting transfer learn- ing with deep neural networks is to fine-tune a model pre- trained on the source task using data from the target task.
Oct 28, 2020 · The method was evaluated against the problem of identifying the osteosarcoma from the medical imaging dataset. The performance was compared ...
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Nov 21, 2018 · In this paper, we propose an adaptive fine-tuning approach, called SpotTune, which finds the optimal fine-tuning strategy per instance for the ...
Nov 10, 2020 · In recent years, this issue has been commonly addressed with the exploitation of transfer learning via fine-tuning, which enables us to start ...
Oct 8, 2020 · Our method is based on convolutional neural networks (CNNs), where we compare how fine-tuning, in the context of transfer learning, from ...
The typical way of conducting transfer learning with deep neural networks is to fine-tune a model pretrained on the source task using data from the target task.
We propose an adaptive fine-tuning approach, called AdaFilter, which selects only a part of the convolutional filters in the pre-trained model to optimize on a ...
We propose an adaptive fine-tuning approach, called AdaFilter, which selects only a part of the convolutional filters in the pre-trained model to optimize on a ...
Transfer learning, which allows a source task to affect the inductive bias of the target task, is widely used in com- puter vision.
The typical way of conducting transfer learning with deep neural networks is to fine-tune a model pretrained on the source task using data from the target task.