Weight Initialization Techniques for Deep Learning Algorithms in Remote Sensing: Recent Trends and Future Perspectives

By Wadii Boulila, Maha Driss, M. Al-Sarem, Faisal Saeed, Moez Krichen. 2021

35 citations recorded by the source corpus.

During the last decade, several research works have focused on providing novel deep learning methods in many application fields. However, few of them have investigated the weight initialization process for deep learning, although its importance is revealed in improving deep learning performance. This can be justified by the technical difficulties in proposing new techniques for this promising research field. In this paper, a survey related to weight initialization techniques for deep algorithms in remote sensing is conducted. This survey will help practitioners to drive further research in this promising field. To the best of our knowledge, this paper constitutes the first survey focusing on weight initialization for deep learning models.

Subfields: Computer Vision and Pattern Recognition, Media Technology, Ocean Engineering

Citations in the corpus

• Deep Reinforcement Learning (2023, unknown): “Artificial neural networks (ANNs) with several layers of interconnected nodes are the foundation of deep learning (DL), a subset of ML that is motivated by the structure and operation of the human brain [3], [4].”