ISOMETRIC REPRESENTATIONS IN NEURAL NET-WORKS IMPROVE ROBUSTNESS

Abstract

Artificial and biological agents are unable to learn given completely random and unstructured data. The structure of data is encoded in the distance or similarity relationships between data points. In the context of neural networks, the neuronal activity within a layer forms a representation reflecting the transformation that the layer implements on its inputs. In order to utilize the structure in the data in a truthful manner, such representations should reflect the input distances and thus be continuous and isometric. Supporting this statement, recent findings in neuroscience propose that generalization and robustness are tied to neural representations being continuously differentiable. However, in machine learning, most algorithms lack robustness and are generally thought to rely on aspects of the data that differ from those that humans use, as is commonly seen in adversarial attacks. During cross-entropy classification, the metric and structural properties of network representations are usually broken both between and within classes. This side effect from training can lead to instabilities under perturbations near locations where such structure is not preserved. One of the standard solutions to obtain robustness is to train specifically by introducing perturbations in the training data. This leads to networks that are particularly robust to specific training perturbations but not necessarily to general perturbations. While adding ad hoc regularization terms to improve robustness has become common practice, to our knowledge, forcing representations to preserve the metric structure of the input data as a stabilising mechanism has not yet been introduced. In this work, we train neural networks to perform classification while simultaneously maintaining the metric structure within each class, leading to continuous and isometric within-class representations. We show that such network representations turn out to be a beneficial component for making accurate and robust inferences about the world. By stacking layers with this property we provide the community with an network architecture that facilitates hierarchical manipulation of internal neural representations. Finally, we verify that our isometric regularization term improves the robustness to adversarial attacks on MNIST.

1. INTRODUCTION

Using neuroscience as an inspiration to enforce properties in machine learning has roots dating back to the birth of artificial neural networks (McCulloch & Pitts, 1943; Rosenblatt, 1958) . One way to study natural and artificial neural networks is to look at how they transform specific structural properties of input data. The output of such a transformation is typically called a neural, or latent, representation, and it carries information about the computational role of a brain region or network layer (Kriegeskorte, 2008; Kriegeskorte & Diedrichsen, 2019; Bengio et al., 2013) . Different properties of representations are helpful in different ways for both organisms and artificial agents. Some examples of this are efficient coding Barlow et al. (1961) , mixed selectivity (Rigotti et al., 2013) , sparse coding (Olshausen & Field, 2004) , response normalization (Carandini & Heeger, 2012) , efficiency and smoothness (Stringer et al., 2019) and expressivity (Poole et al., 2016; Raghu et al., 2017 ) among others. For example, one subsection of theories related to efficient coding proposes that neural circuits should generate discontinuous and high-dimensional representations to pack the most information 1

