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Generative adversarial networks (GANs) usually struggle in learning from highly diverse data, whose underlying manifold is complex.
Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L., Li, K., and Fei-Fei, L · 2009
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Learning multiple layers of features from tiny images
Krizhevsky, A. and Hinton, G · 2009
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Bayesian learning via stochastic gradient langevin dynamics
Welling, M. and Teh, Y. W · 2011
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Deep unsupervised learning using nonequilibrium thermodynamics
Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., and Ganguli, S · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Yu, F., Zhang, Y., Song, S., Seff, A., and Xiao, J · 2015
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Towards principled methods for training generative adversarial networks
Arjovsky, M. and Bottou, L · 2016
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Generative adversarial text to image synthesis
Reed, S. E., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I. J., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Wasserstein generative adversarial networks
Arjovsky, M., Chintala, S., and Bottou, L · 2017
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Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Rai, H. and Shukla, N · 2017
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Multimodal unsupervised image-to-image translation
Huang, X., Liu, M.-Y., Belongie, S. J., and Kautz, J · 2018
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Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2018
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Drit++: Diverse image-to-image translation via disentangled representations
Lee, H.-Y., Tseng, H.-Y., Huang, J.-B., Singh, M. K., and Yang, M.-H · 2018
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Large scale gan training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K · 2019
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Autogan: Neural architecture search for generative adversarial networks
Gong, X., Chang, S., Jiang, Y., and Wang, Z · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T · 2019
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Improved precision and recall metric for assessing generative models
Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., and Aila, T · 2019
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Semantic image synthesis with spatially-adaptive normalization
Park, T., Liu, M.-Y., Wang, T.-C., and Zhu, J.-Y · 2019
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Interpreting the latent space of gans for semantic face editing
Shen, Y., Gu, J., Tang, X., and Zhou, B · 2019
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Sliced wasserstein generative models
Wu, J., Huang, Z., Acharya, D., Li, W., Thoma, J., Paudel, D., and Van Gool, L · 2019
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On the ”steerability” of generative adversarial networks
Ali, J., Lucy, C., and Phillip, I · 2020
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Denoising diffusion probabilistic models
Ho, J., Jain, A., and Abbeel, P · 2020
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Analyzing and improving the image quality of stylegan
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T · 2020
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Contrastive learning for unpaired image-to-image translation
Park, T., Efros, A. A., Zhang, R., and Zhu, J.-Y · 2020
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Closed-form factorization of latent semantics in gans
Shen, Y. and Zhou, B · 2020
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Score-based generative modeling through stochastic differential equations
Song, Y., Sohl-Dickstein, J., Kingma, D. P., Kumar, A., Ermon, S., and Poole, B · 2020
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Dpm-solver: A fast ode solver for diffusion probabilistic model sampling in around 10 steps
Lu, C., Zhou, Y., Bao, F., Chen, J., Li, C., and Zhu, J · 2022
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SDEdit: Guided image synthesis and editing with stochastic differential equations
Meng, C., He, Y., Song, Y., Song, J., Wu, J., Zhu, J.-Y., and Ermon, S · 2022
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Hierarchical text-conditional image generation with clip latents
Ramesh, A., Dhariwal, P., Nichol, A., Chu, C., and Chen, M · 2022
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Photorealistic text-to-image diffusion models with deep language understanding
Saharia, C., Chan, W., Saxena, S., Li, L., Whang, J., Denton, E. L., Ghasemipour, S. K. S., Ayan, B. K., Mahdavi, S. S., Lopes, R. G., Salimans, T., Ho, J., Fleet, D. J., and Norouzi, M · 2022
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Progressive distillation for fast sampling of diffusion models
Salimans, T. and Ho, J · 2022
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Off-policy reinforcement learning for efficient and effective gan architecture search
Tian, Y., Wang, Q., Huang, Z., Li, W., Dai, D., Yang, M., Wang, J., and Fink, O · 2020
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pi-gan: Periodic implicit generative adversarial networks for 3d-aware image synthesis
Chan, E., Monteiro, M., Kellnhofer, P., Wu, J., and Wetzstein, G · 2021
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Ilvr: Conditioning method for denoising diffusion probabilistic models
Choi, J., Kim, S., Jeong, Y., Gwon, Y., and Yoon, S · 2021
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Diffusion models beat GANs on image synthesis
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Transgan: Two pure transformers can make one strong gan, and that can scale up
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Alias-free generative adversarial networks
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Stylegan-xl: Scaling stylegan to large diverse datasets
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Hammer: An efficient toolkit for training deep models
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Improving 3d-aware image synthesis with a geometry-aware discriminator
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Diffusion-gan: Training gans with diffusion
Wang, Z., Zheng, H., He, P., Chen, W., and Zhou, M · 2022
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Tackling the generative learning trilemma with denoising diffusion GANs
Xiao, Z., Kreis, K., and Vahdat, A · 2022
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Poisson flow generative models
Xu, Y., Liu, Z., Tegmark, M., and Jaakkola, T. S · 2022
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Diffedit: Diffusion-based semantic image editing with mask guidance
Couairon, G., Verbeek, J., Schwenk, H., and Cord, M · 2023
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Dreamfusion: Text-to-3d using 2d diffusion
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Learning 3d-aware image synthesis with unknown pose distribution
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Improved techniques for training consistency models
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Fast sampling of diffusion models with exponential integrator
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Truncated diffusion probabilistic models
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