mlsanityData to 7 Oct 2026

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

arXiv 2102.07004 ↗First posted 13 Feb 2021cs.LG cs.AIcited by 8

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Abstract (arXiv)

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.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 4 of its 36 references that are in the map. First posted on arXiv 13 Feb 2021.

Builds on

4 of 36 references.

References · 4

  1. 2018Recurrently Exploring Class-wise Attention in A Hybrid Convolutional and Bidirectional LSTM Network for Multi-label Aerial Image Classificationcited by 17
  2. 2018Infrastructure Quality Assessment in Africa using Satellite Imagery and Deep Learningcited by 6
  3. 2019Pixel-Wise PolSAR Image Classification via a Novel Complex-Valued Deep Fully Convolutional Networkcited by 6
  4. 2017EddyNet: A Deep Neural Network For Pixel-Wise Classification of Oceanic Eddiescited by 5

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Citing papers per year: 2022: 3; 2023: 1; 2024: 3; 2025: 1.

8 papers.