Transfer Learning using Neural Ordinary Differential Equations

• URL: http://arxiv.org/abs/2001.07342v1
• Date: Tue, 21 Jan 2020 04:59:08 GMT
• Title: Transfer Learning using Neural Ordinary Differential Equations
• Authors: Rajath S, Sumukh Aithal K, Natarajan Subramanyam
• Abstract summary: We use EfficientNets to explore transfer learning on CIFAR-10 dataset. Using NODE for fine tuning provides more stability during training and validation.
• Score: 0.32228025627337864
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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