Data Limited Scenario

Data-limited scenarios in machine learning address the challenge of training effective models with insufficient labeled data, a common problem across various domains. Current research focuses on techniques like synthetic data generation using generative models (e.g., VAEs, GANs), knowledge distillation from pre-trained models, and data augmentation strategies (e.g., mixed-sample augmentation, RandAugment variants). These methods aim to improve model robustness and generalization, particularly in applications like healthcare and finance where large datasets are often unavailable. The development of effective solutions in this area is crucial for expanding the applicability of machine learning to a wider range of real-world problems.