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

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