MO-EMT-NAS: Multi-Objective Continuous Transfer of Architectural Knowledge Between Tasks from Different Datasets

TL;DR

MO-EMT-NAS introduces a multi-objective evolutionary framework that transfers architectural knowledge across diverse datasets and tasks, improving model performance and efficiency in multi-task neural architecture search.

Contribution

It proposes a novel multi-objective transfer method for NAS that handles tasks from different datasets and mitigates the small model trap problem.

Findings

Achieves better minimum classification error compared to state-of-the-art methods.

Reduces runtime by up to 77.7% compared to single-task approaches.

Effectively balances model accuracy and computational efficiency.

Abstract

Deploying models across diverse devices demands tradeoffs among multiple objectives due to different resource constraints. Arguably, due to the small model trap problem in multi-objective neural architecture search (MO-NAS) based on a supernet, existing approaches may fail to maintain large models. Moreover, multi-tasking neural architecture search (MT-NAS) excels in handling multiple tasks simultaneously, but most existing efforts focus on tasks from the same dataset, limiting their practicality in real-world scenarios where multiple tasks may come from distinct datasets. To tackle the above challenges, we propose a Multi-Objective Evolutionary Multi-Tasking framework for NAS (MO-EMT-NAS) to achieve architectural knowledge transfer across tasks from different datasets while finding Pareto optimal architectures for multi-objectives, model accuracy and computational efficiency. To alleviate the small model trap issue, we introduce an auxiliary objective that helps maintain multiple larger models of similar accuracy. Moreover, the computational efficiency is further enhanced by parallelizing the training and validation of the weight-sharing-based supernet. Experimental results on seven datasets with two, three, and four task combinations show that MO-EMT-NAS achieves a better minimum classification error while being able to offer flexible trade-offs between model performance and complexity, compared to the state-of-the-art single-objective MT-NAS algorithms. The runtime of MO-EMT-NAS is reduced by 59.7% to 77.7%, compared to the corresponding multi-objective single-task approaches.