Multiobjective Deep Learning

Published in University of California, Los Angeles on 2019

Recommended citation: Jayanth, Jayanth. Multiobjective Deep Learning. University of California, Los Angeles, 2019.

Many current challenges in natural language processing and computer vision have to deal with multiple objectives simultaneously. In this article, we study different methods to solve such multi-objective problem for CIFAR-100 and SEMEVAL datasets, and compare with traditional deep learning methods. The multi-output method achieves better results than training a single neural net from scratch with its own model for each objective. Multi-objective deep learning with weights achieves comparable results too.

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