Surrogate-assisted Particle Swarm Optimisation for Evolving Variable-length Transferable Blocks for Image Classification

TL;DR

This paper introduces a surrogate-assisted particle swarm optimization method to efficiently evolve convolutional neural network blocks for image classification, significantly reducing computational costs while maintaining high accuracy and transferability.

Contribution

It proposes a novel surrogate model, dataset creation method, and encoding strategy integrated into PSO, enabling efficient neural architecture search with transferability across datasets.

Findings

Achieved 3.49% error on CIFAR-10

Evolved blocks transferred successfully to CIFAR-100 and SVHN

Completed search within 3 GPU-days

Abstract

Deep convolutional neural networks have demonstrated promising performance on image classification tasks, but the manual design process becomes more and more complex due to the fast depth growth and the increasingly complex topologies of convolutional neural networks. As a result, neural architecture search has emerged to automatically design convolutional neural networks that outperform handcrafted counterparts. However, the computational cost is immense, e.g. 22,400 GPU-days and 2,000 GPU-days for two outstanding neural architecture search works named NAS and NASNet, respectively, which motivates this work. A new effective and efficient surrogate-assisted particle swarm optimisation algorithm is proposed to automatically evolve convolutional neural networks. This is achieved by proposing a novel surrogate model, a new method of creating a surrogate dataset and a new encoding strategy to encode variable-length blocks of convolutional neural networks, all of which are integrated into a particle swarm optimisation algorithm to form the proposed method. The proposed method shows its effectiveness by achieving competitive error rates of 3.49% on the CIFAR-10 dataset, 18.49% on the CIFAR-100 dataset, and 1.82% on the SVHN dataset. The convolutional neural network blocks are efficiently learned by the proposed method from CIFAR-10 within 3 GPU-days due to the acceleration achieved by the surrogate model and the surrogate dataset to avoid the training of 80.1% of convolutional neural network blocks represented by the particles. Without any further search, the evolved blocks from CIFAR-10 can be successfully transferred to CIFAR-100 and SVHN, which exhibits the transferability of the block learned by the proposed method.