Hard Sample Generation

Hard sample generation focuses on improving the training of machine learning models by strategically selecting challenging examples that push model boundaries. Current research explores techniques like curriculum learning, which gradually introduces harder samples, and methods that generate synthetic hard samples using generative adversarial networks or other sampling strategies within retrieval augmented generation frameworks. These advancements aim to enhance model robustness and accuracy, particularly in applications with limited or noisy data, such as medical image analysis and object detection, leading to improved performance in various domains.