Siamese Network

Siamese networks are a class of neural networks designed to learn a similarity function between pairs of inputs, primarily used for tasks like image comparison, object tracking, and similarity search. Current research focuses on enhancing Siamese network architectures, such as integrating transformers and attention mechanisms, and applying them to diverse domains including medical image analysis, remote sensing, and natural language processing. This approach offers significant advantages in scenarios with limited labeled data or a need for efficient similarity comparisons, impacting fields ranging from automated visual inspection to personalized medicine.