Regularization for Unconditional Image Diffusion Models via Shifted Data Augmentation
Diffusion models are a powerful class of techniques in ML for generating realistic data, but they are highly prone to overfitting, especially with limited training data. While data augmentation such as image rotation can mitigate this issue, it often causes leakage, where augmented content appears i...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
|
| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11048911/ |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849415661505216512 |
|---|---|
| author | Kensuke Nakamura Bong-Soo Sohn Simon Korman Byung-Woo Hong |
| author_facet | Kensuke Nakamura Bong-Soo Sohn Simon Korman Byung-Woo Hong |
| author_sort | Kensuke Nakamura |
| collection | DOAJ |
| description | Diffusion models are a powerful class of techniques in ML for generating realistic data, but they are highly prone to overfitting, especially with limited training data. While data augmentation such as image rotation can mitigate this issue, it often causes leakage, where augmented content appears in generated samples. In this paper, we propose a novel regularization framework, called shifted data-augmentation (SDA), for training unconditional diffusion models. SDA introduces an auxiliary diffusion path using transformed data and the noise-shift technique alongside the standard path with original data. This dual-path structure enables effective regularization while suppressing leakage through a trajectory shift in the diffusion process. We implement SDA with image rotation as its most basic and interpretable configuration. We also conduct synthetic and empirical analyses demonstrating that SDA effectively leverages the regularization benefit of image rotation. In particular, SDA yielded notable performance in training with limited data. |
| format | Article |
| id | doaj-art-13170482bc8d4e828c95c2aac97c3cb3 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-13170482bc8d4e828c95c2aac97c3cb32025-08-20T03:33:27ZengIEEEIEEE Access2169-35362025-01-011311325811327310.1109/ACCESS.2025.358275611048911Regularization for Unconditional Image Diffusion Models via Shifted Data AugmentationKensuke Nakamura0https://orcid.org/0000-0002-6858-3551Bong-Soo Sohn1https://orcid.org/0000-0003-4656-5659Simon Korman2Byung-Woo Hong3https://orcid.org/0000-0003-2752-3939Artificial Intelligence Department, Chung-Ang University, Seoul, South KoreaComputer Science Department, Chung-Ang University, Seoul, South KoreaDepartment of Computer Science, University of Haifa, Haifa, IsraelArtificial Intelligence Department, Chung-Ang University, Seoul, South KoreaDiffusion models are a powerful class of techniques in ML for generating realistic data, but they are highly prone to overfitting, especially with limited training data. While data augmentation such as image rotation can mitigate this issue, it often causes leakage, where augmented content appears in generated samples. In this paper, we propose a novel regularization framework, called shifted data-augmentation (SDA), for training unconditional diffusion models. SDA introduces an auxiliary diffusion path using transformed data and the noise-shift technique alongside the standard path with original data. This dual-path structure enables effective regularization while suppressing leakage through a trajectory shift in the diffusion process. We implement SDA with image rotation as its most basic and interpretable configuration. We also conduct synthetic and empirical analyses demonstrating that SDA effectively leverages the regularization benefit of image rotation. In particular, SDA yielded notable performance in training with limited data.https://ieeexplore.ieee.org/document/11048911/Data augmentationdiffusion modelnoise-shiftregularization |
| spellingShingle | Kensuke Nakamura Bong-Soo Sohn Simon Korman Byung-Woo Hong Regularization for Unconditional Image Diffusion Models via Shifted Data Augmentation IEEE Access Data augmentation diffusion model noise-shift regularization |
| title | Regularization for Unconditional Image Diffusion Models via Shifted Data Augmentation |
| title_full | Regularization for Unconditional Image Diffusion Models via Shifted Data Augmentation |
| title_fullStr | Regularization for Unconditional Image Diffusion Models via Shifted Data Augmentation |
| title_full_unstemmed | Regularization for Unconditional Image Diffusion Models via Shifted Data Augmentation |
| title_short | Regularization for Unconditional Image Diffusion Models via Shifted Data Augmentation |
| title_sort | regularization for unconditional image diffusion models via shifted data augmentation |
| topic | Data augmentation diffusion model noise-shift regularization |
| url | https://ieeexplore.ieee.org/document/11048911/ |
| work_keys_str_mv | AT kensukenakamura regularizationforunconditionalimagediffusionmodelsviashifteddataaugmentation AT bongsoosohn regularizationforunconditionalimagediffusionmodelsviashifteddataaugmentation AT simonkorman regularizationforunconditionalimagediffusionmodelsviashifteddataaugmentation AT byungwoohong regularizationforunconditionalimagediffusionmodelsviashifteddataaugmentation |