Switch Diffusion Transformer: Synergizing Denoising Tasks with Sparse Mixture-of-Experts
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By optimizing decisions and policies through interactive feedback loops, reinforcement learning has established itself as a crucial tool for training AI systems in scenarios demanding high adaptability and precision in decision-making.
“By optimizing decisions and policies through interactive feedback loops, reinforcement learning has established itself as a crucial tool for training AI systems in scenarios that demand a high degree of adaptability and precision in decision-making [184], [185].”
From From Google Gemini to OpenAI Q* (Q-Star): A Survey on Reshaping the Generative Artificial Intelligence (AI) Research Landscape (2025) · cites this paper as [184]
Cites
U-Net: Convolutional Networks for Biomedical Image Segmentation
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ImageNet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., & Fei-Fei, L. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206848 · Cited by 65,333 · see citing works
High-Resolution Image Synthesis with Latent Diffusion Models
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., & Ommer, B. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.01042 · Cited by 15,880 · see citing works
A Style-Based Generator Architecture for Generative Adversarial Networks
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Adaptive Mixtures of Local Experts
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Scalable Diffusion Models with Transformers
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Instructpix2pix: Learning to follow image editing instructions
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All are Worth Words: A ViT Backbone for Diffusion Models
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Perception Prioritized Training of Diffusion Models
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Efficient Diffusion Training via Min-SNR Weighting Strategy
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Masked Diffusion Transformer is a Strong Image Synthesizer
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McIntosh, T. R., Susnjak, T., Liu, T., Watters, P., Xu, D., Liu, D., & Halgamuge, M. N. (2025) · Technologies · doi:10.3390/technologies13020051 · Cited by 102 · see citing works
- Authors
- Park, B., Go, H., Kim, J.-Y., Woo, S., Ham, S., & Kim, C.
- Affiliations
- Korea Advanced Institute of Science and Technology (KR)
- Corresponding author
- Park, Byeongjun
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Nature Switzerland
- Published
- 2024-12-01
- Pages
- 461-477
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W4404889670
- Cited by
- 9
- References
- 18
- Citations by year
- 2026: 3 · 2025: 6
- FWCI
- 1.86
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Licence
- Locations
- Lecture notes in computer science (publishedVersion)
- arXiv (Cornell University) (submittedVersion, cc-by)
- arXiv (Cornell University) (cc-by)
- Funders
- National Research Foundation
- National Research Foundation of Korea (NRF-2021M3I1A1097906)
- Korea Customs Service
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Image and Signal Denoising Methods
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
- Keywords
- diffusion models, sparse mixture-of-experts, multi-task learning, image denoising, parameter isolation, task routing, diffusion transformer
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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