- The paper introduces a taxonomy that integrates diffusion models with representation learning to boost both generative and recognition tasks.
- It demonstrates how techniques like intermediate activation extraction and knowledge transfer enhance image synthesis and semantic segmentation.
- It identifies future research directions including refined architectures and disentangled representations to further advance model performance.
Diffusion Models and Representation Learning: A Survey
The paper "Diffusion Models and Representation Learning: A Survey" by Michael Fuest et al. provides a comprehensive examination of the intersection between diffusion models and representation learning. Diffusion models have recently gained prominence in generative modeling, particularly within the vision domain. This survey elucidates various methodologies that leverage diffusion models for representation learning and vice versa, structuring these approaches into coherent taxonomies and frameworks while identifying gaps and future research directions.
Overview of Diffusion Models
Diffusion models, typified by works such as DDPM \cite{ho2020denoising}, have set new benchmarks in generative modeling across diverse modalities. These models utilize a noise-adding forward process combined with a corresponding reverse denoising process, parametrized by neural networks, to generate data from noise. Architectures like U-Net and transformers serve as common backbones for these denoising networks, enabling high-quality image synthesis when guided by conditioning signals.
Leveraging Diffusion Models for Representation Learning
The paper discusses several paradigms that utilize pre-trained diffusion models for downstream recognition tasks:
Methods like DDPM-Seg \cite{baranchuk_label-efficient_2022} extract intermediate activations from the U-Net architecture trained in diffusion models. These activations are rich in semantic content and can be used for tasks like semantic segmentation. Further research by \citet{xiang_denoising_2023} extends the evaluation of such methods to various backbones and tasks, demonstrating the robustness of diffusion-based representation learning.
Knowledge Transfer Approaches
These involve distilling learned representations from diffusion models into auxiliary networks. RepFusion \cite{yang_diffusion_2023} utilizes reinforcement learning to dynamically extract useful features, while DreamTeacher \cite{li_dreamteacher_2023} employs a feature regressor to transfer knowledge to a target image recognition backbone. These methods significantly enhance downstream recognition performance by leveraging the rich feature space learned by diffusion models.
Diffusion Model Reconstruction
By deconstructing and modifying diffusion models, methods like latent DAE (l-DAE) \cite{chen_deconstructing_2024} explore the underlying components responsible for effective representation learning. Other approaches like DiffAE \cite{preechakul2022diffusion_autoencoder} separate semantic and stochastic representations to enhance both encoding and decoding processes.
Joint Representation and Generation Models
HybViT \cite{yang_your_2022} and JDM \cite{deja_learning_2023} integrate both generative and discriminative objectives into a unified model. These approaches reveal that leveraging shared parametrizations for generation and recognition can yield models that perform competently in both domains while simplifying the training process.
Representation Learning for Diffusion Model Guidance
Representation learning techniques can significantly benefit the guidance mechanisms in diffusion models, often enhancing the quality and control of generated outputs:
Assignment-Based Guidance
Techniques like self-guided diffusion \cite{hu_self-guided_2023} and online guidance \cite{hu_guided_2023} employ self-supervised feature extractors and clustering methods to generate pseudo-labels, which guide the diffusion process without requiring the annotated data typically necessary for classifier-free guidance.
Representation-Based Guidance
RCG \cite{li_return_2024} proposes training a separate diffusion model on self-supervised representations to generate control signals for a subsequent pixel generator. This method bridges the gap between supervised and unsupervised image generation by effectively using learned representations as guidance signals.
Objective-Based Guidance
SGCIG \cite{epstein_diffusion_2023} and DAG \cite{kim_depth-aware_2024} leverage internal representations and introduce new guidance terms to aid the generation control. These terms can be steered to adjust specific attributes like depth and semantic content, providing enhanced control over the generated images.
Implications and Future Directions
This survey accentuates the mutual benefits of combining diffusion models with representation learning. On a theoretical level, it elucidates the architectural and operational synergies that potentiate both generative and discriminative capabilities. Practically, it suggests promising pathways for improving diffusion model performance via enhanced representation learning techniques and vice versa.
Exploring interpretable and disentangled representations, refining backbone architectures, and extending these paradigms to other generative frameworks like Flow Matching models are identified as potential future research directions. Addressing these areas will be crucial for unlocking new capabilities and applications for generative and discriminative models alike.
In summary, the interplay between diffusion models and representation learning ushers in a new era of versatile and high-performing AI models, promising advancements across diverse domains within artificial intelligence.