One Shot

"One-shot" learning in machine learning focuses on training models to perform tasks (like object recognition, image generation, or action recognition) using only a single example per class, drastically reducing data requirements. Current research emphasizes efficient model architectures, such as Siamese networks, Vision Transformers, and diffusion models, often incorporating techniques like contrastive learning and multi-scale feature matching to improve performance with limited data. This area is significant because it addresses the limitations of data-hungry models, enabling applications in resource-constrained environments and facilitating rapid adaptation to new tasks or objects in robotics, medical imaging, and other fields.