A Constructive Prediction of the Generalization Error Across Scales

Jonathan S. Rosenfeld, Amir Rosenfeld, Yonatan Belinkov, Nir Shavit

TL;DR: We predict the generalization error and specify the model which attains it across model/data scales.

Abstract: The dependency of the generalization error of neural networks on model and dataset size is of critical importance both in practice and for understanding the theory of neural networks. Nevertheless, the functional form of this dependency remains elusive. In this work, we present a functional form which approximates well the generalization error in practice. Capitalizing on the successful concept of model scaling (e.g., width, depth), we are able to simultaneously construct such a form and specify the exact models which can attain it across model/data scales. Our construction follows insights obtained from observations conducted over a range of model/data scales, in various model types and datasets, in vision and language tasks. We show that the form both fits the observations well across scales, and provides accurate predictions from small- to large-scale models and data.

Keywords: neural networks, deep learning, generalization error, scaling, scalability, vision, language

Data: [CIFAR-10](https://paperswithcode.com/dataset/cifar-10), [CIFAR-100](https://paperswithcode.com/dataset/cifar-100), [DTD](https://paperswithcode.com/dataset/dtd), [UCF101](https://paperswithcode.com/dataset/ucf101), [WikiText-103](https://paperswithcode.com/dataset/wikitext-103), [WikiText-2](https://paperswithcode.com/dataset/wikitext-2)

Community Implementations: [ 2 code implementations](https://www.catalyzex.com/paper/arxiv:1909.12673/code)