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Diffusion models represent a powerful family of generative models widely used for image and video generation.
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2021
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J. Pool and C. Yu, “Channel permutations for n: M sparsity,” Advances in neural information processing systems , vol. 34, pp. 13 316–13 327, 2021
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A. Zhou, Y. Ma, J. Zhu, J. Liu, Z. Zhang, K. Yuan, W. Sun, and H. Li, “Learning n: M fine-grained structured sparse neural networks from scratch,” in International Conference on Learning Representations , 2021
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K. Wang, H. Xiong, J. Bian, Z. Zhu, Q. Gao, Z. Guo, C.-Z. Xu, J. Huan, and D. Dou, “Sampling sparse representations with randomized measurement langevin dynamics,” ACM Transactions on Knowledge Discovery from Data (TKDD) , vol. 15, no. 2, pp. 1–21, 2021
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K. Wang, H. Xiong, J. Zhang, H. Chen, D. Dou, and C.-Z. Xu, “Sensemag: Enabling low-cost traffic monitoring using noninvasive magnetic sensing,” IEEE Internet of Things Journal , vol. 8, no. 22, pp. 16 666–16 679, 2021
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T. Karras, M. Aittala, T. Aila, and S. Laine, “Elucidating the design space of diffusion-based generative models,” Advances in Neural Information Processing Systems , vol. 35, pp. 26 565–26 577, 2022
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C. Lu, Y. Zhou, F. Bao, J. Chen, C. Li, and J. Zhu, “Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps,” Advances in Neural Information Processing Systems , vol. 35, pp. 5775–5787, 2022
2022
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T. Salimans and J. Ho, “Progressive distillation for fast sampling of diffusion models,” in International Conference on Learning Representations , 2022
2022
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Y. Zhang, M. Lin, Z. Lin, Y. Luo, K. Li, F. Chao, Y. Wu, and R. Ji, “Learning best combination for efficient n: M sparsity,” Advances in Neural Information Processing Systems , vol. 35, pp. 941–953, 2022
2022
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R. Rombach, A. Blattmann, D. Lorenz, P. Esser, and B. Ommer, “High-resolution image synthesis with latent diffusion models,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2022, pp. 10 684–10 695
2022
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Y. Gu, H. Cheng, K. Wang, D. Dou, C. Xu, and H. Kong, “Learning moving-object tracking with fmcw lidar,” in 2022 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2022, pp. 3747–3753
2022
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K. Zheng, C. Lu, J. Chen, and J. Zhu, “Dpm-solver-v3: Improved diffusion ode solver with empirical model statistics,” in Thirty-seventh Conference on Neural Information Processing Systems , 2023
2023
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T. Castells, H.-K. Song, B.-K. Kim, and S. Choi, “Ld-pruner: Efficient pruning of latent diffusion models using task-agnostic insights,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshop , 2024, pp. 821–830
2024
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2024
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Z. S. Jiang, X. Han, H. Jin, G. Wang, R. Chen, N. Zou, and X. Hu, “Chasing fairness under distribution shift: A model weight perturbation approach,” Advances in Neural Information Processing Systems , vol. 36, 2024
2024
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L. Yan, X. Zhang, K. Wang, and D. Zhang, “Contour-enhanced visual state-space model for remote sensing image classification,” IEEE Transactions on Geoscience and Remote Sensing , 2024
2024
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L.-y. Yan, X. Zhang, K. Wang, S. Xiong, and D.-j. Zhang, “Image segmentation refinement based on region expansion and minor contour adjustments,” IET Image Processing , vol. 19, no. 1, p. e70017, 2025
2025
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B. Zhang, H. Xu, R. Shuang, and K. Wang, “Heterogeneous information-based self-supervised graph learning for recommendation,” The Journal of Supercomputing , vol. 81, no. 4, p. 507, 2025
2025
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