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Classifier-free guidance has become a staple for conditional generation with denoising diffusion models.
Equation of State Calculations by Fast Computing Machines
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Generalized Accept-Reject Sampling Schemes
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ImageNet: A Large-Scale Hierarchical Image Database
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Learning Multiple Layers of Features from Tiny Images
Krizhevsky, A. 2009 · 2009
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A Connection Between Score Matching and Denoising Autoencoders
Vincent, P. 2011 · 2011
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Adam: A Method for Stochastic Optimization
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Auto-Encoding Variational Bayes
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Fine-grained visual categorization via multi-stage metric learning
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U-Net: Convolutional Networks for Biomedical Image Segmentation
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GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
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Decoupled Weight Decay Regularization
Loshchilov, I.; and Hutter, F. 2017 · 2017
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Large Scale GAN Training for High Fidelity Natural Image Synthesis
Brock, A.; Donahue, J.; and Simonyan, K. 2018 · 2018
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Variational Rejection Sampling
Grover, A.; Gummadi, R.; Lázaro-Gredilla, M.; Schuurmans, D.; and Ermon, S. 2018 · 2018
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A Style-Based Generator Architecture for Generative Adversarial Networks
Karras, T.; Laine, S.; and Aila, T. 2018 · 2018
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Glow: Generative Flow with Invertible 1x1 Convolutions
Kingma, D. P.; and Dhariwal, P. 2018 · 2018
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Image Transformer
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Metropolis-Hastings Generative Adversarial Networks
Turner, R. D.; Hung, J.; Saatci, Y.; and Yosinski, J. 2018 · 2018
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The Unreasonable Effectiveness of Deep Features as a Perceptual Metric
Zhang, R.; Isola, P.; Efros, A. A.; Shechtman, E.; and Wang, O. 2018 · 2018
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Discriminator Rejection Sampling
Azadi, S.; Olsson, C.; Darrell, T.; Goodfellow, I. J.; and Odena, A. 2019 · 2019
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Resampled Priors for Variational Autoencoders
Bauer, M.; and Mnih, A. 2019 · 2019
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Generating Diverse High-Fidelity Images with VQ-VAE-2
Razavi, A.; van den Oord, A.; and Vinyals, O. 2019 · 2019
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Generative Modeling by Estimating Gradients of the Data Distribution
Song, Y.; and Ermon, S. 2019 · 2019
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PyTorch Image Models
Wightman, R. 2019 · 2019
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Language Models are Few-Shot Learners
Brown, T. B.; Mann, B.; Ryder, N.; Subbiah, M.; Kaplan, J.; Dhariwal, P.; Neelakantan, A.; Shyam, P.; Sastry, G.; Askell, A.; Agarwal, S.; Herbert-Voss, A.; Krueger, G.; Henighan, T.; Child, R.; Ramesh, A.; Ziegler, D. M.; Wu, J.; Winter, C.; Hesse, C.; Chen, M.; Sigler, E.; teusz Litwin, M.; Gray, S.; Chess, B.; Clark, J.; Berner, C.; McCandlish, S.; Radford, A.; Sutskever, I.; and Amodei, D. 2020 · 2020
Refining Generative Process with Discriminator Guidance in Score-based Diffusion Models
Kim, D.; Kim, Y.; Kang, W.; and Moon, I.-C. 2022 · 2022
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GPT-4 Technical Report
Achiam, J.; Adler, S.; Agarwal, S.; Ahmad, L.; Akkaya, I.; Aleman, F. L.; Almeida, D.; Altenschmidt, J.; Altman, S.; Anadkat, S.; et al. 2023 · 2023
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Vision Transformers Need Registers
Darcet, T.; Oquab, M.; Mairal, J.; and Bojanowski, P. 2023 · 2023
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Improving Sample Quality of Diffusion Models Using Self-Attention Guidance
Hong, S.; Lee, G.; Jang, W.; and Kim, S. W. 2023 · 2023
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Reparameterized Variational Rejection Sampling
Jankowiak, M.; and Phan, D. 2023 · 2023
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Flow Matching for Generative Modeling
Lipman, Y.; Chen, R. T. Q.; Ben-Hamu, H.; Nickel, M.; and Le, M. 2023 · 2023
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Cited alongside, same era.
Your GAN is Secretly an Energy-based Model and You Should use Discriminator Driven Latent Sampling
Che, T.; Zhang, R.; Sohl-Dickstein, J. N.; Larochelle, H.; Paull, L.; Cao, Y.; and Bengio, Y. 2020 · 2020
Cited alongside, same era.
Accelerating Large-Scale Inference with Anisotropic Vector Quantization
Guo, R.; Sun, P.; Lindgren, E.; Geng, Q.; Simcha, D.; Chern, F.; and Kumar, S. 2020 · 2020
Cited alongside, same era.
Denoising Diffusion Probabilistic Models
Ho, J.; Jain, A.; and Abbeel, P. 2020 · 2020
Cited alongside, same era.
A Contrastive Learning Approach for Training Variational Autoencoder Priors
Aneja, J.; Schwing, A. G.; Kautz, J.; and Vahdat, A. 2021 · 2021
Cited alongside, same era.
WaveGrad: Estimating Gradients for Waveform Generation
Chen, N.; Zhang, Y.; Zen, H.; Weiss, R. J.; Norouzi, M.; and Chan, W. 2021 · 2021
Cited alongside, same era.
Diffusion Models Beat GANs on Image Synthesis
Dhariwal, P.; and Nichol, A. 2021 · 2021
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Flow Straight and Fast: Learning to Generate and Transfer Data with Rectified Flow
Liu, X.; Gong, C.; and Liu, Q. 2023 · 2023
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DINOv2: Learning Robust Visual Features without Supervision
Oquab, M.; Darcet, T.; Moutakanni, T.; Vo, H. V.; Szafraniec, M.; Khalidov, V.; Fernandez, P.; Haziza, D.; Massa, F.; El-Nouby, A.; Howes, R.; Huang, P.-Y.; Xu, H.; Sharma, V.; Li, S.-W.; Galuba, W.; Rabbat, M.; Assran, M.; Ballas, N.; Synnaeve, G.; Misra, I.; Jegou, H.; Mairal, J.; Labatut, P.; Joulin, A.; and Bojanowski, P. 2023 · 2023
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DreamFusion: Text-to-3D using 2D Diffusion
Poole, B.; Jain, A.; Barron, J. T.; and Mildenhall, B. 2023 · 2023
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Exposing flaws of generative model evaluation metrics and their unfair treatment of diffusion models
Stein, G.; Cresswell, J. C.; Hosseinzadeh, R.; Sui, Y.; Ross, B. L.; Villecroze, V.; Liu, Z.; Caterini, A. L.; Taylor, J. E. T.; and Loaiza-Ganem, G. 2023 · 2023
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SOAR: Improved Indexing for Approximate Nearest Neighbor Search
Sun, P.; Simcha, D.; Dopson, D.; Guo, R.; and Kumar, S. 2023 · 2023
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Classifier-Free Guidance is a Predictor-Corrector
Bradley, A.; and Nakkiran, P. 2024 · 2024
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What Does Guidance Do? A Fine-Grained Analysis in a Simple Setting
Chidambaram, M.; Gatmiry, K.; Chen, S.; Lee, H.; and Lu, J. 2024 · 2024
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CFG++: Manifold-Constrained Classifier Free Guidance for Diffusion Models
Chung, H.; Kim, J.; Park, G. Y.; Nam, H.; and Ye, J. C. 2024 · 2024
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The FAISS Library
Douze, M.; Guzhva, A.; Deng, C.; Johnson, J.; Szilvasy, G.; Mazaré, P.-E.; Lomeli, M.; Hosseini, L.; and Jégou, H. 2024 · 2024
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The Llama 3 Herd of Models
Dubey, A.; Jauhri, A.; Pandey, A.; Kadian, A.; Al-Dahle, A.; Letman, A.; Mathur, A.; Schelten, A.; Yang, A.; Fan, A.; et al. 2024 · 2024
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Diffusion Model with Perceptual Loss
Lin, S.; and Yang, X. 2024 · 2024
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CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling
Sadat, S.; Buhmann, J.; Bradley, D.; Hilliges, O.; and Weber, R. M. 2024 · 2024
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Improving and Generalizing Flow-Based Generative Models With Minibatch Optimal Transport
Tong, A.; Malkin, N.; Huguet, G.; Zhang, Y.; Rector-Brooks, J.; Fatras, K.; Wolf, G.; and Bengio, Y. 2024 · 2024
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Theoretical Insights for Diffusion Guidance: A Case Study for Gaussian Mixture Models
Wu, Y.; Chen, M.; Li, Z.; Wang, M.; and Wei, Y. 2024 · 2024
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Rectified Diffusion Guidance for Conditional Generation
Xia, M.; Xue, N.; Shen, Y.; Yi, R.; Gong, T.; and Liu, Y.-J. 2024 · 2024
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