Learning Deep Morphological Networks with Neural Architecture Search

Over the last decade, deep learning has made several breakthroughs and demonstrated successful applications in various fields (e.g. in computer vision Krizhevsky et al. [2012], Simonyan and Zisserman [2014a], He et al. [2016a], Huang et al. [2017], object detection Redmon et al. [2016], or NLP Dai et al. [2019], Radford et al. [2019]). This success is mainly due to its automation of the feature engineering process. This success is mainly attributable to the fact that it automates the feature engineering process. Rather than manually designed features, features are learned in an end-to-end process from data. The need for improved architecture has swiftly followed the advent of deep learning. Experts now place a premium on architecture engineering in lieu of features engineering. Architecture engineering is concerned with determining the most appropriate operations for the network, their hyperparameters (e.g. the number of neurons for fully connected layers, or the number of filters or kernel size for convolutional layers), and the connectivity of all the operations. Generally, practitioners propose novel operations to validate various architectures and tasks in order to improve performance on specific tasks. As a result, developing a novel operation remains a time-consuming and costly process.