Shot Training

Few-shot learning aims to train effective machine learning models using extremely limited labeled data, addressing the significant challenge of data scarcity in many real-world applications. Current research focuses on improving model performance through techniques like adapting prior distributions in generative models, leveraging pre-trained models (e.g., CLIP, DINO) and their knowledge via prompting and transfer learning, and developing novel architectures and algorithms (e.g., transformers, prototypical networks) to better handle limited training examples. These advancements are crucial for deploying AI systems in domains where acquiring large labeled datasets is expensive or impractical, impacting fields such as personalized object recognition, anomaly detection, and cross-lingual natural language processing.