Latent Distillation

Latent distillation is a machine learning technique focused on transferring knowledge from a complex "teacher" model to a simpler, more efficient "student" model, often by leveraging latent representations. Current research emphasizes applications in diverse areas, including medical image analysis, privacy-preserving learning, and accelerating generative models like diffusion models and probabilistic circuits, employing techniques such as generative knowledge distillation and variational autoencoders. This approach offers significant advantages in model compression, improved robustness (e.g., against adversarial attacks), and enhanced generalization across datasets, impacting fields ranging from healthcare to computer vision.