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...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2025-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/11048911/ |
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