Unraveling Dropout: Classifying Dropout Methods in the Context of Research Challenges Across Data Types

Overview

This review examines how dropout methods are used across data types and research settings, organizing their design choices, strengths, and trade-offs. It highlights that regularization can behave differently for images, text, and structured data, so the choice of dropout strategy should reflect the task and model. The manuscript is currently under review.