MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models

TL;DR

This paper introduces MEESO, an automated, multi-objective approach for building deep learning models that balances accuracy and uncertainty, reducing manual effort and computational costs.

Contribution

The paper presents a novel multi-objective end-to-end self-optimization framework for automatic deep learning model construction, supporting diverse model selection and trade-offs.

Findings

Achieves competitive accuracy on MNIST, Fashion, and Cifar10 datasets.

Provides multi-objective trade-off solutions for accuracy and uncertainty.

Reduces manual effort and computational resources in deep learning model development.

Abstract

Deep learning has been widely used in various applications from different fields such as computer vision, natural language processing, etc. However, the training models are often manually developed via many costly experiments. This manual work usually requires substantial computing resources, time, and experience. To simplify the use of deep learning and alleviate human effort, automated deep learning has emerged as a potential tool that releases the burden for both users and researchers. Generally, an automatic approach should support the diversity of model selection and the evaluation should allow users to decide upon their demands. To that end, we propose a multi-objective end-to-end self-optimized approach for constructing deep learning models automatically. Experimental results on well-known datasets such as MNIST, Fashion, and Cifar10 show that our algorithm can discover various competitive models compared with the state-of-the-art approach. In addition, our approach also introduces multi-objective trade-off solutions for both accuracy and uncertainty metrics for users to make better decisions.