Novel applications of Convolutional Neural Networks in the age of Transformers

Abstract

Convolutional Neural Networks (CNNs) have been central to the Deep Learning revolution and played a key role in initiating the new age of Artificial Intelligence. However, in recent years newer architectures such as Transformers have dominated both research and practical applications. While CNNs still play critical roles in many of the newer developments such as Generative AI, they are far from being thoroughly understood and utilised to their full potential. Here we show that CNNs can recognise patterns in images with scattered pixels and can be used to analyse complex datasets by transforming them into pseudo images in a standardised way for any high dimensional dataset, representing a major advance in the application of CNNs to datasets such as in molecular biology, text, and speech. We introduce a simple approach called DeepMapping, which allows analysis of very high dimensional datasets without intermediate filtering and dimension reduction, thus preserving the full texture of the data, enabling the ability to detect small perturbations. We also demonstrate that DeepMapper is superior in speed and on par in accuracy to prior work in processing large datasets with large numbers of features.