Cross Domain Few Shot Learning

Cross-domain few-shot learning (CDFSL) tackles the challenge of training machine learning models to recognize new categories with limited examples, especially when the training and testing data come from different sources (domains). Current research focuses on improving model adaptability across domains, often employing techniques like meta-learning, transformer networks, and data augmentation methods such as mixup and style transfer to bridge the domain gap. CDFSL is significant because it addresses the limitations of traditional few-shot learning, enabling more robust and efficient model training in real-world scenarios where labeled data is scarce and domains vary, with applications ranging from object detection to medical image analysis.