The Utility of Feature Reuse: Transfer Learning in Data-Starved Regimes

• URL: http://arxiv.org/abs/2003.04117v2
• Date: Thu, 28 Dec 2023 15:53:41 GMT
• Title: The Utility of Feature Reuse: Transfer Learning in Data-Starved Regimes
• Authors: Rashik Shadman, M.G. Sarwar Murshed, Edward Verenich, Alvaro Velasquez, Faraz Hussain
• Abstract summary: We describe a transfer learning use case for a domain with a data-starved regime. We evaluate the effectiveness of convolutional feature extraction and fine-tuning. We conclude that transfer learning enhances the performance of CNN architectures in data-starved regimes.
• Score: 6.419457653976053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The use of transfer learning with deep neural networks has increasingly become widespread for deploying well-tested computer vision systems to newer domains, especially those with limited datasets. We describe a transfer learning use case for a domain with a data-starved regime, having fewer than 100 labeled target samples. We evaluate the effectiveness of convolutional feature extraction and fine-tuning of overparameterized models with respect to the size of target training data, as well as their generalization performance on data with covariate shift, or out-of-distribution (OOD) data. Our experiments demonstrate that both overparameterization and feature reuse contribute to the successful application of transfer learning in training image classifiers in data-starved regimes. We provide visual explanations to support our findings and conclude that transfer learning enhances the performance of CNN architectures in data-starved regimes.

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