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

arXiv: https://arxiv.org/abs/2003.04117 · HTML: https://mlsanity.com/paper/2003.04117/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Feb 2020.

## Built on by (most cited first, 1 of 1)

- [Task Agnostic Architecture for Algorithm Induction via Implicit Composition](https://mlsanity.com/paper/2404.02450/) (2024)

## Abstract (arXiv)

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.