Physics-Inspired Graph Classifier Achieves High ImageNet Accuracy with Reduced Computation

Global: Physics-Inspired Graph Classifier Achieves High ImageNet Accuracy with Reduced Computation

A recent arXiv preprint (arXiv:2508.18717) introduces a physics‑inspired graph‑based classification pipeline that compresses MobileNetV2 feature vectors while delivering competitive top‑1 accuracy on ImageNet subsets. The authors report that the approach reduces floating‑point operations dramatically compared with conventional deep‑learning baselines.

Method Overview

The pipeline treats frozen MobileNetV2 embeddings as Ising spins placed on a sparse Multi‑Edge Type QC‑LDPC graph, effectively forming a Random Bond Ising Model. System parameters are tuned to the Nishimori temperature, identified by the vanishing of the smallest Bethe‑Hessian eigenvalue.

Key Innovations

Two technical contributions are highlighted. First, the authors prove a spectral‑topological correspondence that links graph trapping sets to invariants derived from the Ihara‑Bass zeta function; eliminating these structures reportedly boosts top‑1 accuracy by more than four‑fold in multi‑class settings. Second, they develop a quadratic‑Newton estimator for the Nishimori temperature that converges in roughly nine Arnoldi iterations, providing an estimated six‑times speedup for spectral embedding on large‑scale datasets such as ImageNet‑100.

Efficiency Gains

The described estimator enables rapid computation of the temperature parameter, allowing the method to scale to high‑dimensional image collections without prohibitive computational cost.

Dimensionality Reduction

Using the graph‑based representation, the authors compress the original 1280‑dimensional MobileNetV2 features to 32 dimensions for the ImageNet‑10 benchmark and to 64 dimensions for ImageNet‑100.

Performance Benchmarks

Experimental results show a top‑1 accuracy of 98.7% on ImageNet‑10 and 84.92% on ImageNet‑100 when employing a three‑graph soft ensemble. Compared with a hard ensemble of MobileNetV2, the new method increases top‑1 accuracy by 0.1% while cutting FLOPs by 2.67‑times relative to ResNet‑50. The soft ensemble variant reduces FLOPs by 29‑times with only a 1.09% drop in top‑1 accuracy.

Implications for Deployment

According to the authors, the results demonstrate that topology‑guided LDPC embedding can produce highly compressed yet accurate classifiers, making them suitable for resource‑constrained environments such as edge devices.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.