Shot Approach

Few-shot learning aims to train machine learning models effectively using very limited labeled data, addressing the challenge of data scarcity in many domains. Current research focuses on improving model performance through techniques like meta-learning, prompt engineering, and the integration of pre-trained models (e.g., transformers, convolutional neural networks) with novel architectures designed for few-shot scenarios. This field is significant because it enables the application of machine learning to tasks with limited annotated data, impacting diverse areas such as natural language processing, computer vision, and drug discovery.