CollaFuse: Collaborative Diffusion Models

arXiv:2406.14429 · cs.LG, cs.AI, cs.CV · Submitted 2024-06-20 · Read on arXiv

cs.LG, cs.AI, cs.CV

Submitted: 2024-06-20

Updated: 2026-08-27

Code: https://github.com/SimeonAllmendinger/collafuse

Terminology

• Diffusion Model — 64× in this paper · explained in 8 episodes
• Diffusion Models — 56× in this paper · explained in 6 episodes
• Federated Learning — 21× in this paper · explained in 14 episodes
• Fidelity — 17× in this paper · explained in 2 episodes
• Diffusion Process — 6× in this paper · explained in 2 episodes
• Generative AI — 6× in this paper · explained in 2 episodes
• Generative Model — 6× in this paper · explained in 2 episodes
• Generative Models — 6× in this paper · explained in 2 episodes
• Global Model — 6× in this paper
• Denoising Diffusion Probabilistic Models — 4× in this paper
• Information Leakage — 4× in this paper · explained in 2 episodes
• International Conference on Learning Representations — 4× in this paper
• Ablation — 3× in this paper · explained in 1 episode
• Adam — 3× in this paper · explained in 2 episodes
• Adaptation — 3× in this paper · explained in 1 episode
• Differentially Private — 3× in this paper · explained in 3 episodes
• Distillation — 3× in this paper · explained in 2 episodes
• Embeddings — 3× in this paper · explained in 2 episodes
• Personalization — 3× in this paper · explained in 2 episodes
• Quantization — 3× in this paper · explained in 12 episodes
• Accuracy — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Benchmark — 2× in this paper · explained in 2 episodes
• Data Heterogeneity — 2× in this paper · explained in 3 episodes
• Federated Learning (FL) — 2× in this paper · explained in 7 episodes
• Frechet CLIP Distance — 2× in this paper
• International Conference on Computer Vision — 2× in this paper
• Knowledge Distillation — 2× in this paper · explained in 11 episodes
• Latent Diffusion Model — 2× in this paper · explained in 2 episodes
• Latent Diffusion Models — 2× in this paper · explained in 2 episodes
• Latent Space — 2× in this paper · explained in 7 episodes
• Loss Function — 2× in this paper · explained in 2 episodes
• Ablation Studies — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Communication Overhead — 1× in this paper · explained in 2 episodes
• Computational overhead — 1× in this paper · explained in 2 episodes
• Conference on Computer Vision and Pattern Recognition — 1× in this paper
• Continual Learning — 1× in this paper · explained in 2 episodes
• Differential Privacy — 1× in this paper · explained in 4 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Foundation Model — 1× in this paper · explained in 3 episodes
• Foundation Models — 1× in this paper · explained in 7 episodes
• Frechet Inception Distance — 1× in this paper
• Generalization — 1× in this paper · explained in 13 episodes
• Generative Adversarial Networks (GANs) — 1× in this paper · explained in 3 episodes
• Interpretability — 1× in this paper · explained in 15 episodes
• Latent Diffusion Models (LDMs) — 1× in this paper · explained in 2 episodes
• Latent Representation — 1× in this paper · explained in 2 episodes
• Latent Representations — 1× in this paper · explained in 3 episodes
• Multi-objective Optimization — 1× in this paper · explained in 4 episodes
• Non-IID Data — 1× in this paper · explained in 2 episodes
• Transfer Learning — 1× in this paper · explained in 10 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• U-net architecture — 1× in this paper · explained in 3 episodes
• Variational Bayes — 1× in this paper · explained in 2 episodes

Sources

• Deep Learning for Classical Japanese Literature
• Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms

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