Siamese Sleep Transformer

Siamese sleep transformers are a type of neural network architecture increasingly used for tasks requiring comparison and analysis of paired data, particularly in time-series analysis like sleep stage scoring and image restoration. Current research focuses on improving robustness and efficiency through techniques such as self-knowledge distillation, selective batch sampling, and joint alignment/regression strategies within the Siamese framework, often incorporating transformer encoders and decoders for effective feature extraction. This approach shows promise for applications needing robust performance despite noisy or incomplete data, impacting fields ranging from healthcare (sleep analysis) to remote sensing (change detection) and image processing.