U(1) Symmetry-breaking Observed in Generic CNN Bottleneck Layers

TL;DR

This paper models CNN bottleneck layers using an analogy to optical systems and particle physics, revealing a U(1) symmetry-breaking bias in classification tasks that improves accuracy when incorporated into training.

Contribution

It introduces a novel geometric and physical analogy for CNN bottleneck layers, uncovering symmetry-breaking phenomena that enhance classification performance.

Findings

U(1) symmetry-breaking observed in CNN bottleneck layers

Inclusion of U(1) bias improves classification accuracy

Model validated on pre-trained and trained-from-scratch CNNs

Abstract

We report on a novel model linking deep convolutional neural networks (CNN) to biological vision and fundamental particle physics. Information propagation in a CNN is modeled via an analogy to an optical system, where information is concentrated near a bottleneck where the 2D spatial resolution collapses about a focal point $1\times 1=1$. A 3D space $(x,y,t)$ is defined by $(x,y)$ coordinates in the image plane and CNN layer $t$, where a principal ray $(0,0,t)$ runs in the direction of information propagation through both the optical axis and the image center pixel located at $(x,y)=(0,0)$, about which the sharpest possible spatial focus is limited to a circle of confusion in the image plane. Our novel insight is to model the principal optical ray $(0,0,t)$ as geometrically equivalent to the medial vector in the positive orthant $I(x,y) \in R^{N+}$ of a $N$-channel activation space, e.g. along the greyscale (or luminance) vector $(t,t,t)$ in $RGB$ colour space. Information is thus concentrated into an energy potential $E(x,y,t)=\|I(x,y,t)\|^2$, which, particularly for bottleneck layers $t$ of generic CNNs, is highly concentrated and symmetric about the spatial origin $(0,0,t)$ and exhibits the well-known "Sombrero" potential of the boson particle. This symmetry is broken in classification, where bottleneck layers of generic pre-trained CNN models exhibit a consistent class-specific bias towards an angle $\theta \in U(1)$ defined simultaneously in the image plane and in activation feature space. Initial observations validate our hypothesis from generic pre-trained CNN activation maps and a bare-bones memory-based classification scheme, with no training or tuning. Training from scratch using combined one-hot $+ U(1)$ loss improves classification for all tasks tested including ImageNet.