A Geometric Perspective on Diffusion Models
暂无分享,去创建一个
[1] Seung Wook Kim,et al. Align Your Latents: High-Resolution Video Synthesis with Latent Diffusion Models , 2023, 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[2] Dian Ang Yap,et al. TRACT: Denoising Diffusion Models with Transitive Closure Time-Distillation , 2023, ArXiv.
[3] T. Jaakkola,et al. Stable Target Field for Reduced Variance Score Estimation in Diffusion Models , 2023, ICLR.
[4] K. Azizzadenesheli,et al. Fast Sampling of Diffusion Models via Operator Learning , 2022, ICML.
[5] Ben Poole,et al. DreamFusion: Text-to-3D using 2D Diffusion , 2022, ICLR.
[6] Cheng Lu,et al. DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps , 2022, NeurIPS.
[7] Tero Karras,et al. Elucidating the Design Space of Diffusion-Based Generative Models , 2022, NeurIPS.
[8] David J. Fleet,et al. Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding , 2022, NeurIPS.
[9] Yongxin Chen,et al. Fast Sampling of Diffusion Models with Exponential Integrator , 2022, ICLR.
[10] Prafulla Dhariwal,et al. Hierarchical Text-Conditional Image Generation with CLIP Latents , 2022, ArXiv.
[11] David J. Fleet,et al. Video Diffusion Models , 2022, NeurIPS.
[12] Tim Salimans,et al. Progressive Distillation for Fast Sampling of Diffusion Models , 2022, ICLR.
[13] B. Ommer,et al. High-Resolution Image Synthesis with Latent Diffusion Models , 2021, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[14] Diederik P. Kingma,et al. Variational Diffusion Models , 2021, ArXiv.
[15] Jan Kautz,et al. Score-based Generative Modeling in Latent Space , 2021, NeurIPS.
[16] Prafulla Dhariwal,et al. Diffusion Models Beat GANs on Image Synthesis , 2021, NeurIPS.
[17] Eric Luhman,et al. Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed , 2021, ArXiv.
[18] Abhishek Kumar,et al. Score-Based Generative Modeling through Stochastic Differential Equations , 2020, ICLR.
[19] Jiaming Song,et al. Denoising Diffusion Implicit Models , 2020, ICLR.
[20] O. Papaspiliopoulos. High-Dimensional Probability: An Introduction with Applications in Data Science , 2020 .
[21] Bryan Catanzaro,et al. DiffWave: A Versatile Diffusion Model for Audio Synthesis , 2020, ICLR.
[22] Heiga Zen,et al. WaveGrad: Estimating Gradients for Waveform Generation , 2020, ICLR.
[23] Pieter Abbeel,et al. Denoising Diffusion Probabilistic Models , 2020, NeurIPS.
[24] Yang Song,et al. Generative Modeling by Estimating Gradients of the Data Distribution , 2019, NeurIPS.
[25] Aapo Hyvärinen,et al. Neural Empirical Bayes , 2019, J. Mach. Learn. Res..
[26] Sepp Hochreiter,et al. GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium , 2017, NIPS.
[27] Trevor Hastie,et al. Computer Age Statistical Inference: Algorithms, Evidence, and Data Science , 2016 .
[28] Yinda Zhang,et al. LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop , 2015, ArXiv.
[29] Surya Ganguli,et al. Deep Unsupervised Learning using Nonequilibrium Thermodynamics , 2015, ICML.
[30] Michael S. Bernstein,et al. ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.
[31] Yoshua Bengio,et al. What regularized auto-encoders learn from the data-generating distribution , 2012, J. Mach. Learn. Res..
[32] Christian P. Robert,et al. Large-scale inference , 2010 .
[33] B. Efron. Tweedie’s Formula and Selection Bias , 2011, Journal of the American Statistical Association.
[34] Pascal Vincent,et al. A Connection Between Score Matching and Denoising Autoencoders , 2011, Neural Computation.
[35] Eero P. Simoncelli,et al. Least Squares Estimation Without Priors or Supervision , 2011, Neural Computation.
[36] Yoshua Bengio,et al. Extracting and composing robust features with denoising autoencoders , 2008, ICML '08.
[37] Miguel Á. Carreira-Perpiñán,et al. Gaussian Mean-Shift Is an EM Algorithm , 2007, IEEE Transactions on Pattern Analysis and Machine Intelligence.
[38] Aapo Hyvärinen,et al. Estimation of Non-Normalized Statistical Models by Score Matching , 2005, J. Mach. Learn. Res..
[39] Anton van den Hengel,et al. Fast global kernel density mode seeking with application to localization and tracking , 2005, Tenth IEEE International Conference on Computer Vision (ICCV'05) Volume 1.
[40] J. S. Marron,et al. Geometric representation of high dimension, low sample size data , 2005 .
[41] Dorin Comaniciu,et al. Mean Shift: A Robust Approach Toward Feature Space Analysis , 2002, IEEE Trans. Pattern Anal. Mach. Intell..
[42] Yizong Cheng,et al. Mean Shift, Mode Seeking, and Clustering , 1995, IEEE Trans. Pattern Anal. Mach. Intell..
[43] B. Øksendal. Stochastic differential equations : an introduction with applications , 1987 .
[44] C. Morris. Parametric Empirical Bayes Inference: Theory and Applications , 1983 .
[45] B. Anderson. Reverse-time diffusion equation models , 1982 .
[46] Alex Krizhevsky,et al. Learning Multiple Layers of Features from Tiny Images , 2009 .
[47] Hassan K. Khalil,et al. Nonlinear Systems Third Edition , 2008 .
[48] Roger Wattenhofer. Consistency of models , 1980 .
[49] Larry D. Hostetler,et al. The estimation of the gradient of a density function, with applications in pattern recognition , 1975, IEEE Trans. Inf. Theory.
[50] H. Robbins. An Empirical Bayes Approach to Statistics , 1956 .