Multiobjective Based Strategy for Neural Architecture Search for Segmentation Task

This research aims to determine an optimal neural network model for image segmentation, addressing a crucial aspect of computer vision and deep learning. The study aims to identify high-performance neural network architectures characterized by superior accuracy and minimized complexity through a combination of deep learning principles, multi-objective optimization and genetic algorithms. The proposed approach adapts NSGA-III to generate new neural network architectures encoded via binary representation. The chromosomes are then decoded to undergo the training. The focus of this work is to explore and identify better models based on performance metrics: intersection over union, accuracy, and frequency weighted intersection over union (fwIoU), while simultaneously minimizing model complexity by optimizing the number of parameters. The primary results are very encouraging, and in future work, we aim to provide more tests and analysis on other benchmarks.