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

• URL: http://arxiv.org/abs/2102.07004v1
• Date: Sat, 13 Feb 2021 21:21:16 GMT
• Title: Weight Initialization Techniques for Deep Learning Algorithms in Remote Sensing: Recent Trends and Future Perspectives
• Authors: Wadii Boulila, Maha Driss, Mohamed Al-Sarem, Faisal Saeed, Moez Krichen
• Abstract summary: This paper constitutes the first survey focusing on weight initialization for deep learning models. It will help practitioners to drive further research in this promising field.
• Score: 0.9624643581968988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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