Jul 30, 2018 · Transfer Learning can deliver higher accuracy if the hyperparameters and source dataset are chosen well. One of the important parameters is the ...
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In this paper, we delve into the components of DNN architectures and propose Transferable Normalization (TransNorm) in place of existing normalization ...
Jan 9, 2020 · Transfer Learning can deliver higher accuracy if the hyperparameters and source dataset are chosen well.
The goal of TransNorm is to improve the transferability of deep networks with simple and efficient network layers easily pluggable into the network backbones of ...
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It is shown through experiments on the ImageNet22k and Oxford Flowers datasets that improvements in accuracy in range of 127% can be obtained by proper ...
Transfer Learning can deliver higher accuracy if the hyperparameters and source dataset are chosen well. One of the important parameters is the learning rate ...
Towards Improving Transferability of Deep Neural Networks - NIPS papers
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The paper proposes a novel normalization strategies in the line of AdaBN and AutoDIAL paper. The method is simple, easy to use and shows better results than ...
TransNorm is an end-to-end trainable layer to make DNNs more transferable across domains. As a general method, TransNorm can be easily applied to various deep ...
Improving the adversarial robustness and interpretability of deep neural networks by regularizing their input gradients. arXiv preprint arXiv:1711.09404 ...
TransNorm is an end-to-end trainable layer to make DNNs more transferable across domains and can be easily applied to various deep neural networks and ...