The Compact Support Neural Network

TL;DR

This paper introduces a novel neural network with compact support neurons that improve reliability and out-of-distribution detection, combining theoretical guarantees with empirical performance improvements.

Contribution

It proposes a new neuron shape parameterization with compact support, a training method from pretrained networks, and proves universal approximation capabilities.

Findings

Smaller test errors than state-of-the-art methods.

Better out-of-distribution detection on most datasets.

Theoretical bounds on neuron gradient and universal approximation.

Abstract

Neural networks are popular and useful in many fields, but they have the problem of giving high confidence responses for examples that are away from the training data. This makes the neural networks very confident in their prediction while making gross mistakes, thus limiting their reliability for safety-critical applications such as autonomous driving, space exploration, etc. This paper introduces a novel neuron generalization that has the standard dot-product-based neuron and the {\color{black} radial basis function (RBF)} neuron as two extreme cases of a shape parameter. Using a rectified linear unit (ReLU) as the activation function results in a novel neuron that has compact support, which means its output is zero outside a bounded domain. To address the difficulties in training the proposed neural network, it introduces a novel training method that takes a pretrained standard neural network that is fine-tuned while gradually increasing the shape parameter to the desired value. The theoretical findings of the paper are a bound on the gradient of the proposed neuron and a proof that a neural network with such neurons has the universal approximation property. This means that the network can approximate any continuous and integrable function with an arbitrary degree of accuracy. The experimental findings on standard benchmark datasets show that the proposed approach has smaller test errors than state-of-the-art competing methods and outperforms the competing methods in detecting out-of-distribution samples on two out of three datasets.