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arXiv · 2001.07342

Transfer Learning using Neural Ordinary Differential Equations

Rajath S·Sumukh Aithal K·Natarajan Subramanyam
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Abstract

A concept of using Neural Ordinary Differential Equations(NODE) for Transfer Learning has been introduced. In this paper we use the EfficientNets to explore transfer learning on CIFAR-10 dataset. We use NODE for fine-tuning our model. Using NODE for fine tuning provides more stability during training and validation.These continuous depth blocks can also have a trade off between numerical precision and speed .Using Neural ODEs for transfer learning has resulted in much stable convergence of the loss function.

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Rajath S, Sumukh Aithal K, Natarajan Subramanyam. 2020-01-21. Transfer Learning using Neural Ordinary Differential Equations. https://arxiv.org/abs/2001.07342

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