Diffusion Autoencoder

Diffusion autoencoders (DAEs) combine the strengths of diffusion probabilistic models and autoencoders to learn rich, interpretable latent representations of data, primarily images but also extending to other modalities like point clouds and even temporal data such as videos and stock prices. Current research focuses on improving the disentanglement of latent factors for controllable generation and manipulation, developing novel architectures like hierarchical DAEs and incorporating techniques such as contrastive learning to enhance semantic meaning. DAEs are proving valuable for various applications, including medical image analysis (e.g., Alzheimer's disease classification, fracture grading), neuroimaging data harmonization, and high-fidelity image generation and editing, offering advantages over traditional generative models in terms of stability, sample quality, and interpretability.