# A practical generalization metric for deep networks benchmarking

## TL;DR

This paper introduces a practical metric to evaluate how well deep learning models generalize, revealing a gap between theory and practice.

## Contribution

A novel generalization metric and benchmark testbed for evaluating deep networks' generalization capacity.

## Key findings

- Generalization in deep networks depends on classification accuracy and unseen data diversity.
- Most theoretical generalization estimates do not align with practical measurements.
- The proposed metric provides an intuitive trade-off between accuracy and data diversity.

## Abstract

There is an ongoing and dedicated effort to estimate bounds on the generalization error of deep learning models, coupled with an increasing interest with practical metrics that can be used to experimentally evaluate a model’s ability to generalize. This interest is not only driven by practical considerations but is also vital for theoretical research, as theoretical estimations require practical validation. However, there is currently a lack of research on benchmarking the generalization capacity of various deep networks and verifying these theoretical estimations. This paper aims to introduce a practical generalization metric for benchmarking different deep networks and proposes a novel testbed for the verification of theoretical estimations. Our findings indicate that a deep network’s generalization capacity in classification tasks is contingent upon both classification accuracy and the diversity of unseen data. The proposed metric system is capable of quantifying the accuracy of deep learning models and the diversity of data, providing an intuitive and quantitative evaluation method - a trade-off point. Furthermore, we compare our practical metric with existing generalization theoretical estimations using our benchmarking testbed. It is discouraging to note that most of the available generalization estimations do not correlate with the practical measurements obtained using our testbed. On the other hand, this finding is significant as it exposes the shortcomings of theoretical estimations and inspires new exploration.