Distillation Loss

Distillation loss is a technique used to transfer knowledge from a large, complex "teacher" model to a smaller, more efficient "student" model, primarily aiming to improve the student's performance and reduce computational costs. Current research focuses on refining distillation loss functions, exploring various architectures (including vision transformers and convolutional neural networks), and addressing challenges like imbalanced datasets and mitigating bias. This technique is significant for improving the efficiency and accessibility of various machine learning applications, ranging from image recognition and natural language processing to medical image analysis and resource-constrained environments.