Diffusion Adaptation

Diffusion adaptation is a technique leveraging diffusion models to improve the robustness and adaptability of machine learning models, particularly in handling out-of-distribution data or unexpected distribution shifts. Current research focuses on enhancing the accuracy and generalization capabilities of these methods, often employing techniques like marginal entropy loss, contrastive loss, and guidance-based approaches within the diffusion process, across various model architectures including convolutional neural networks. This approach shows promise in improving the performance of models in diverse applications, ranging from image classification under noisy conditions to distributed signal processing and even crowd control simulations, by adapting either the input data or the model's behavior without extensive retraining.