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Diffusion Models and Representation Learning: A Survey
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Diffusion Models are popular generative modeling methods in various vision tasks, attracting significant attention. They can be considered a unique instance of self-supervised learning methods due to their independence from label annotation. This survey explores the interplay between diffusion models and representation learning. It provides an overview of diffusion models' essential aspects, including mathematical foundations, popular denoising network architectures, and guidance methods. Various approaches related to diffusion models and representation learning are detailed. These include frameworks that leverage representations learned from pre-trained diffusion models for subsequent recognition tasks and methods that utilize advancements in representation and self-supervised learning to enhance diffusion models. This survey aims to offer a comprehensive overview of the taxonomy between diffusion models and representation learning, identifying key areas of existing concerns and potential exploration. Github link: https://github.com/dongzhuoyao/Diffusion-Representation-Learning-Survey-Taxonomy
Forward citations
Cited by 7 Pith papers
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Guiding Registration with Emergent Similarity from Pre-Trained Diffusion Models
Diffusion model features, applied as an LNCC similarity loss, improve deformable registration when anatomies are missing in one of the images.
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SCFlow: Implicitly Learning Style and Content Disentanglement with Flow Models
SCFlow learns a reversible style-content merge and then lets the same mapping perform separation without explicit disentanglement training.
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Contrastive Conditional-Unconditional Alignment for Long-tailed Diffusion Model
A two-part training regularizer, an unconditional-only contrastive repulsion plus a large-timestep conditional-unconditional alignment, improves tail-class diversity and fidelity in diffusion models, cutting ImageNet-...
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DiffNMR: Diffusion Models for Nuclear Magnetic Resonance Spectra Elucidation
DiffNMR uses a discrete graph diffusion model conditioned on NMR spectra to predict molecular structures, achieving 68.26% top-1 accuracy with formula on molecules up to 15 heavy atoms.
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Unlocking the Power of Diffusion Models in Sequential Recommendation: A Simple and Effective Approach
ADRec applies token-level, per-token diffusion with causal attention to sequential recommendation, reducing embedding collapse and outperforming ten baselines on six datasets.
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SeaLion: Semantic Part-Aware Latent Point Diffusion Models for 3D Generation
A latent diffusion model that jointly generates 3D point clouds and their semantic part segmentations, plus a part-aware Chamfer distance metric for evaluating them.
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Diff2Flow: Training Flow Matching Models via Diffusion Model Alignment
A reparameterization recipe that lets pre-trained Stable Diffusion checkpoints be finetuned as flow matching models, giving faster convergence and better performance under parameter-efficient constraints.
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