Unified Self-Supervised Representation Learning with Diffusion Models

Discriminative representation learning involves assigning labels to pictures or image sections, while generative learning involves creating or modifying pictures and performing related operations like inpainting and super-resolution. However, there is still a need to address unified self-supervised representation learning, which has not been explored as extensively as self-supervised image representation learning. Some researchers argue that cutting-edge diffusion models, which are powerful image-creation models, already have strong classification capabilities. They suggest that diffusion models can learn unified self-supervised picture representations that perform well in both generation and classification tasks. In summary, their contributions include demonstrating the use of diffusion models as unified representation learners, providing guidelines for utilizing diffusion representations, comparing different classification heads for diffusion models, examining transfer learning characteristics, and comparing diffusion features with alternative architectures and pre-training techniques.