Pre-interpolation loss behaviour in neural networks
This provides incremental insight into a common but poorly understood phenomenon in deep learning, aiding practitioners in network optimization and generalization.
The paper investigates why test loss can increase while classification accuracy improves during neural network training, finding that the loss increase is concentrated in a small minority of samples due to large representational capacities favoring most samples at the expense of others.
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.