Pre-interpolation loss behaviour in neural networks

TL;DR

This paper investigates why test loss can increase during neural network training despite improving accuracy, revealing it results from differential sample processing and capacity effects, with implications for optimization and generalization.

Contribution

It provides an empirical analysis explaining the test loss increase phenomenon and highlights the role of network capacity and parameter changes in this behaviour.

Findings

Test loss increase affects only a small minority of samples.

Large capacity networks decrease loss for most samples but increase it for some.

Parameter changes related to correctly processed features drive the loss behaviour.

Abstract

When training neural networks as classifiers, it is common to observe an increase in average test loss while still maintaining or improving the overall classification accuracy on the same dataset. In spite of the ubiquity of this phenomenon, it has not been well studied and is often dismissively attributed to an increase in borderline correct classifications. We present an empirical investigation that shows how this phenomenon is actually a result of the differential manner by which test samples are processed. In essence: test loss does not increase overall, but only for a small minority of samples. Large representational capacities allow losses to decrease for the vast majority of test samples at the cost of extreme increases for others. This effect seems to be mainly caused by increased parameter values relating to the correctly processed sample features. Our findings contribute to the practical understanding of a common behaviour of deep neural networks. We also discuss the implications of this work for network optimisation and generalisation.