Trajectory Distillation

Trajectory distillation is a technique for compressing large datasets or complex model training processes into more efficient representations, focusing on preserving the essential information needed for high-performance model training. Current research emphasizes methods that optimize the "trajectory" of model training or data generation, often employing techniques like reinforcement learning, gradient matching, and consistency regularization across different model architectures (e.g., diffusion models, transformers, CNNs). This approach offers significant potential for reducing computational costs and storage requirements in various applications, including image and audio processing, and improving the efficiency of semi-supervised learning and domain adaptation in medical imaging.