Generating High Fidelity Data from Low-density Regions using Diffusion Models
Open accessAbstract
Our work focuses on addressing sample deficiency from low-density regions of data manifold in common image datasets. We leverage diffusion process based generative models to synthesize novel images from low-density regions. We observe that uniform sampling from diffusion models predominantly samples from high-density regions of the data manifold. Therefore, we modify the sampling process to guide it towards low-density regions while simulta-neously maintaining the fidelity of synthetic data. We rigorously demonstrate that our process successfully generates novel high fidelity samples from low-density regions. We further examine generated samples and show that the model does not memorize low-density data and indeed learns to generate novel samples from low-density regions.
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Sehwag et al. and Um and Ye enforce the generation process of diffusion models to focus on minority samples such that they can generate high-fidelity samples from low-density regions.
“Sehwag et al. [209] and Um and Ye [237] enforce the generation process of the diffusion models to focus on minority samples such that they can generate high-fidelity samples from low-density regions.”
From Diffusion Models: A Comprehensive Survey of Methods and Applications (2024) · cites this paper as [209]
Cites
Very Deep Convolutional Networks for Large-Scale Image Recognition
Simonyan, K., & Zisserman, A. (2014) · arXiv · doi:10.48550/arxiv.1409.1556 · Cited by 74,248 · see citing works
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
ImageNet Large Scale Visual Recognition Challenge
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al. (2015) · International Journal of Computer Vision · doi:10.1007/s11263-015-0816-y · Cited by 39,380 · see citing works
PyTorch: An Imperative Style, High-Performance Deep Learning Library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al. (2019) · arXiv · doi:10.48550/arxiv.1912.01703 · Cited by 31,473 · see citing works
Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works
Advances in Neural Information Processing Systems 19
(2007) · The MIT Press eBooks · doi:10.52202/085713 · Cited by 16,037 · see citing works
The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., & Wang, O. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00068 · Cited by 14,623 · see citing works
Denoising Diffusion Probabilistic Models
Ho, J., Jain, A., & Abbeel, P. (2020) · arXiv · doi:10.48550/arxiv.2006.11239 · Cited by 8,712 · see citing works
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149 · see citing works
Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A., Donahue, J., & Simonyan, K. (2018) · arXiv · doi:10.48550/arxiv.1809.11096 · Cited by 3,717 · see citing works
Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P., & Nichol, A. (2021) · arXiv · doi:10.48550/arxiv.2105.05233 · Cited by 3,362 · see citing works
Deep Unsupervised Learning using Nonequilibrium Thermodynamics
Sohl-Dickstein, J., Weiss, E. A., Maheswaranathan, N., & Ganguli, S. (2015) · arXiv · doi:10.48550/arxiv.1503.03585 · Cited by 2,637 · see citing works
Denoising Diffusion Implicit Models
Song, J., Meng, C., & Ermon, S. (2020) · arXiv · doi:10.48550/arxiv.2010.02502 · Cited by 2,507 · see citing works
Improved Denoising Diffusion Probabilistic Models
Nichol, A., & Dhariwal, P. (2021) · arXiv · doi:10.48550/arxiv.2102.09672 · Cited by 1,550 · see citing works
Geirhos, R., Rubisch, P., Michaelis, C., Bethge, M., Wichmann, F. A., & Brendel, W. (2018) · arXiv · doi:10.48550/arxiv.1811.12231 · Cited by 1,490 · cited 1× · see citing works
“While proximity in feature space of deep neural networks aligns with human perception [45], deep neural networks are also well known to be biased towards certain attributes, such as texture [12] and background [34, 44].”
BackgroundGenerative Modeling by Estimating Gradients of the Data Distribution
Song, Y., & Ermon, S. (2019) · arXiv · doi:10.48550/arxiv.1907.05600 · Cited by 1,453 · see citing works
The Many Faces of Robustness: A Critical Analysis of Out-of-Distribution Generalization
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00823 · Cited by 1,250 · see citing works
Large-Scale Long-Tailed Recognition in an Open World
Liu, Z., Miao, Z., Zhan, X., Wang, J., Gong, B., & Yu, S. X. (2019) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2019.00264 · Cited by 1,169 · see citing works
Hendrycks, D., Zhao, K., Basart, S., Steinhardt, J., & Song, D. (2021) · 2021 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr46437.2021.01501 · Cited by 1,090 · see citing works
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Child, R., Gray, S., Radford, A., & Sutskever, I. (2019) · arXiv · doi:10.48550/arxiv.1904.10509 · Cited by 1,063 · see citing works
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Sajjadi, M. S. M., Bachem, O., Lučić, M., Bousquet, O., & Gelly, S. (2018) · Neural Information Processing Systems · doi:10.48550/arxiv.1806.00035 · Cited by 815 · see citing works
The Unreasonable Effectiveness of Deep Features as a Perceptual MetricPreprint
Zhang, R., Isola, P., Efros, A. A., et al. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1801.03924 · Cited by 810 · see citing works
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Razavi, A., Oord, A. V. D., & Vinyals, O. (2019) · arXiv · doi:10.48550/arxiv.1906.00446 · Cited by 709 · see citing works
The Secret Sharer: Evaluating and Testing Unintended Memorization in Neural Networks
Carlini, N., Liu, C., Erlingsson, Ú., Kos, J., & Song, D. (2018) · arXiv · doi:10.48550/arxiv.1802.08232 · Cited by 629 · see citing works
Salimans, T., Karpathy, A., Chen, X., & Kingma, D. P. (2017) · arXiv · doi:10.48550/arxiv.1701.05517 · Cited by 584 · see citing works
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Koh, P. W., Sagawa, S., Marklund, H., Xie, S. M., Zhang, M., Balsubramani, A., Hu, W., Yasunaga, M., Phillips, R. L., Gao, I., et al. (2020) · arXiv · doi:10.48550/arxiv.2012.07421 · Cited by 518 · cited 1× · see citing works
“Deep neural networks often struggle to generalize to novel and rarely observed samples from the distribution [16, 20].”
BackgroundCascaded Diffusion Models for High Fidelity Image Generation
Ho, J., Saharia, C., Chan, W., Fleet, D. J., Norouzi, M., & Salimans, T. (2021) · arXiv · doi:10.48550/arxiv.2106.15282 · Cited by 514 · see citing works
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Nalisnick, E., Matsukawa, A., Teh, Y. W., Görür, D., & Lakshminarayanan, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1810.09136 · Cited by 372 · see citing works
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Wang, H., Ge, S., Xing, E. P., & Lipton, Z. C. (2019) · arXiv · doi:10.48550/arxiv.1905.13549 · Cited by 337 · see citing works
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Ding, X., Wang, Z. J., & Welch, W. J. (2020) · IEEE Transactions on Signal Processing · doi:10.1109/tsp.2020.2979601 · Cited by 21 · see citing works
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Li, Q., Mai, L., Alcorn, M. A., & Nguyen, A. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-69538-5_32 · Cited by 6 · see citing works
Li, Q., Mai, L., Alcorn, M. A., et al. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1910.04760 · Cited by 2 · see citing works
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Yang, L., Zhang, Z., Song, Y., Hong, S., Xu, R., Zhao, Y., Zhang, W., Cui, B., & Yang, M.-H. (2024) · ACM Computing Surveys · doi:10.1145/3626235 · Cited by 1,704 · see citing works
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- Authors
- Sehwag, V., Hazirbas, C., Gordo, A., Ozgenel, F., & Ferrer, C. C.
- Affiliations
- Princeton University (US)
- Meta AI
- Meta (United States) (US)
- Venue
- 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2022-06
- Pages
- 11482-11491
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W4312705932
- Cited by
- 41
- References
- 46
- Citations by year
- 2026: 5 · 2025: 11 · 2024: 14 · 2023: 11
- FWCI
- 3.26
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- arXiv (Cornell University)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Generative Adversarial Networks and Image Synthesis
- Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
- Physical Sciences › Engineering › Computational Mechanics › 3D Shape Modeling and Analysis
- Keywords
- diffusion models, low-density areas, data manifold, image generation, generative models
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