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Given a large dataset for training, generative adversarial networks (GANs) can achieve remarkable performance for the image synthesis task.
Image augmentations for GAN training
Zhao, Z., Zhang, Z., Chen, T., Singh, S., Zhang, H., 2020b · 2006
Earlier work this paper cites.
Few-shot adaptation of generative adversarial networks
Robb, E., Chu, W., Kumar, A., Huang, J., 2020 · 2010
Earlier work this paper cites.
Dropout: A simple way to prevent neural networks from overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R., 2014 · 2014
Earlier work this paper cites.
Generating images with perceptual similarity metrics based on deep networks, in: Advances in Neural Information Processing Systems (NeurIPS)
Dosovitskiy, A., Brox, T., 2016 · 2016
Earlier work this paper cites.
A benchmark dataset and evaluation methodology for video object segmentation, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A., 2016 · 2016
Earlier work this paper cites.
YFCC100M: The new data in multimedia research
Thomee, B., Shamma, D.A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., Li, L.J., 2016 · 2016
Earlier work this paper cites.
Image-to-image translation with conditional adversarial networks, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A., 2017 · 2017
Earlier work this paper cites.
Places: A 10 million image database for scene recognition
Zhou, B., Lapedriza, A., Khosla, A., Oliva, A., Torralba, A., 2017 · 2017
Earlier work this paper cites.
Multimodal unsupervised image-to-image translation, in: European Conference on Computer Vision (ECCV)
Huang, X., Liu, M.Y., Belongie, S., Kautz, J., 2018 · 2018
Earlier work this paper cites.
Spectral normalization for generative adversarial networks, in: International Conference on Learning Representations (ICLR)
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y., 2018 · 2018
Earlier work this paper cites.
BAM: Bottleneck attention module, in: British Machine Vision Conference (BMVC)
Park, J., Woo, S., Lee, J.Y., Kweon, I.S., 2018 · 2018
Earlier work this paper cites.
CBAM: Convolutional block attention module, in: European Conference on Computer Vision (ECCV)
Woo, S., Park, J., Lee, J.Y., Kweon, I.S., 2018 · 2018
Earlier work this paper cites.
DropFilter: Dropout for convolutions
Zhengsu, T., Jianwei, C., Qi, N., 2018 · 2018
Earlier work this paper cites.
Large scale GAN training for high fidelity natural image synthesis, in: International Conference on Learning Representations (ICLR)
Brock, A., Donahue, J., Simonyan, K., 2019 · 2019
Earlier work this paper cites.
SinGAN: Learning a generative model from a single natural image, in: International Conference on Computer Vision (ICCV)
Shaham, T.R., Dekel, T., Michaeli, T., 2019 · 2019
Cited alongside, same era.
Disentangling content and style via unsupervised geometry distillation, in: International Conference on Learning Representations (ICLR) workshops
Wu, W., Cao, K., Li, C., Qian, C., Loy, C.C., 2019 · 2019
Cited alongside, same era.
Diversity-sensitive conditional generative adversarial networks, in: International Conference on Learning Representations (ICLR)
Yang, D., Hong, S., Jang, Y., Zhao, T., Lee, H., 2019 · 2019
Cited alongside, same era.
CutMix: Regularization strategy to train strong classifiers with localizable features, in: International Conference on Computer Vision (ICCV)
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y., 2019 · 2019
Cited alongside, same era.
StarGAN v2: Diverse image synthesis for multiple domains, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Few-shot image generation via cross-domain correspondence, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Ojha, U., Li, Y., Lu, J., Efros, A.A., Lee, Y.J., Shechtman, E., Zhang, R., 2021 · 2021
Closest in time.
One-shot gan: Learning to generate samples from single images and videos, in: IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
Sushko, V., Gall, J., Khoreva, A., 2021 · 2021
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Regularizing generative adversarial networks under limited data, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Tseng, H.Y., Jiang, L., Liu, C., Yang, M.H., Yang, W., 2021 · 2021
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Data-efficient instance generation from instance discrimination
Yang, C., Shen, Y., Xu, Y., Zhou, B., 2021 · 2021
Closest in time.
Improved consistency regularization for GANs, in: Conference on Artificial Intelligence (AAAI)
Zhao, Z., Singh, S., Lee, H., Zhang, Z., Odena, A., Zhang, H., 2021 · 2021
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Choi, Y., Uh, Y., Yoo, J., Ha, J.W., 2020 · 2020
Cited alongside, same era.
Denoising diffusion probabilistic models
Ho, J., Jain, A., Abbeel, P., 2020 · 2020
Cited alongside, same era.
MSG-GAN: multi-scale gradient GAN for stable image synthesis, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Karnewar, A., Wang, O., 2020 · 2020
Cited alongside, same era.
Swapping autoencoder for deep image manipulation, in: Advances in Neural Information Processing Systems (NeurIPS)
Park, T., Zhu, J.Y., Wang, O., Lu, J., Shechtman, E., Efros, A.A., Zhang, R., 2020 · 2020
Cited alongside, same era.
Data-efficient GAN training beyond (just) augmentations: A lottery ticket perspective
Chen, T., Cheng, Y., Gan, Z., Liu, J., Wang, Z., 2021 · 2021
Cited alongside, same era.
Diffusion models beat gans on image synthesis
Dhariwal, P., Nichol, A., 2021 · 2021
Cited alongside, same era.
Deceive D: Adaptive pseudo augmentation for GAN training with limited data
Jiang, L., Dai, B., Wu, W., Loy, C.C., 2021 · 2021
Cited alongside, same era.
Alias-free generative adversarial networks
Karras, T., Aittala, M., Laine, S., Härkönen, E., Hellsten, J., Lehtinen, J., Aila, T., 2021 · 2021
Cited alongside, same era.
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Giannone, G., Nielsen, D., Winther, O., 2022 · 2022
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Fine-tuning diffusion models with limited data, in: NeurIPS 2022 Workshop on Score-Based Methods
Moon, T., Choi, M., Lee, G., Ha, J.W., Lee, J., 2022 · 2022
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High-resolution image synthesis with latent diffusion models, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B., 2022 · 2022
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Few shot generative model adaption via relaxed spatial structural alignment, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Xiao, J., Li, L., Wang, C., Zha, Z.J., Huang, Q., 2022 · 2022
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Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation, in: Conference on Computer Vision and Pattern Recognition (CVPR)
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K., 2023 · 2023
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Stylegan-t: Unlocking the power of gans for fast large-scale text-to-image synthesis
Sauer, A., Karras, T., Laine, S., Geiger, A., Aila, T., 2023 · 2023
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One-shot synthesis of images and segmentation masks, in: IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)
Sushko, V., Zhang, D., Gall, J., Khoreva, A., 2023 · 2023
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Patch diffusion: Faster and more data-efficient training of diffusion models
Wang, Z., Jiang, Y., Zheng, H., Wang, P., He, P., Wang, Z., Chen, W., Zhou, M., 2023 · 2023
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