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With the rapid development of Artificial Intelligence Generated Content (AIGC), it has become a common practice to train models on synthetic data due to data-scarcity and privacy leakage problems.
Robbins, H., Monro, S.: A stochastic approximation method. The annals of mathematical statistics, 400–407 (1951)
1951
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, vol. 27 (2014). https://proceedings.neurips.cc/paper_files/paper/2014/file/5ca3e9b122f61f8f06494c97b1afccf3-Paper.pdf
2014
Earlier work this paper cites.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., Zitnick, C.L.: Microsoft coco: Common objects in context. In: Computer Vision–ECCV 2014: 13th European Conference, Zurich, Switzerland, September 6-12, 2014, Proceedings, Part V 13, pp. 740–755 (2014). Springer
2014
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al
2015
Earlier work this paper cites.
Shokri, R., Shmatikov, V.: Privacy-preserving deep learning. In: Proceedings of the 22nd ACM SIGSAC Conference on Computer and Communications Security, pp. 1310–1321 (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)
2016
Earlier work this paper cites.
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: International Conference on Machine Learning, pp. 1060–1069 (2016). PMLR
2016
Earlier work this paper cites.
Zagoruyko, S., Komodakis, N.: Wide residual networks. In: British Machine Vision Conference (2016)
2016
Earlier work this paper cites.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., et al
2017
Earlier work this paper cites.
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223 (2017). PMLR
2017
Earlier work this paper cites.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al
2017
Earlier work this paper cites.
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., Metaxas, D.N.: Stackgan: Text to photo-realistic image synthesis with stacked generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 5907–5915 (2017)
2017
Earlier work this paper cites.
Isola, P., Zhu, J.-Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1125–1134 (2017)
2017
Earlier work this paper cites.
Zhu, J.-Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223–2232 (2017)
2017
Earlier work this paper cites.
Brock, A., Lim, T., Ritchie, J.M., Weston, N.: Neural photo editing with introspective adversarial networks. In: International Conference on Learning Representations (2017). https://openreview.net/forum?id=HkNKFiGex
2017
Earlier work this paper cites.
Shokri, R., Stronati, M., Song, C., Shmatikov, V.: Membership inference attacks against machine learning models. In: 2017 IEEE Symposium on Security and Privacy (SP), pp. 3–18 (2017). IEEE
2017
Earlier work this paper cites.
Krishna, R., Zhu, Y., Groth, O., Johnson, J., Hata, K., Kravitz, J., Chen, S., Kalantidis, Y., Li, L.-J., Shamma, D.A., et al
2017
Earlier work this paper cites.
Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of GANs for improved quality, stability, and variation. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=Hk99zCeAb
2018
Earlier work this paper cites.
Xie, Y., Franz, E., Chu, M., Thuerey, N.: tempogan: A temporally coherent, volumetric gan for super-resolution fluid flow. ACM Transactions on Graphics (TOG) 37
2018
Earlier work this paper cites.
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=B1QRgziT-
2018
Earlier work this paper cites.
Yeom, S., Giacomelli, I., Fredrikson, M., Jha, S.: Privacy risk in machine learning: Analyzing the connection to overfitting. In: 2018 IEEE 31st Computer Security Foundations Symposium (CSF), pp. 268–282 (2018). IEEE
2018
Earlier work this paper cites.
Zhang, H., Cisse, M., Dauphin, Y.N., Lopez-Paz, D.: mixup: Beyond empirical risk minimization. In: International Conference on Learning Representations (2018). https://openreview.net/forum?id=r1Ddp1-Rb
2018
Earlier work this paper cites.
Ravuri, S., Vinyals, O.: Seeing is Not Necessarily Believing: Limitations of BigGANs for Data Augmentation (2019). https://openreview.net/forum?id=rJMw747l_4
2019
Earlier work this paper cites.
Howard, J.: Imagenette: A smaller subset of 10 easily classified classes from Imagenet. GitHub (2019). https://github.com/fastai/imagenette
2019
Earlier work this paper cites.
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: International Conference on Learning Representations (2019). https://openreview.net/forum?id=B1xsqj09Fm
2019
Cited alongside, same era.
Razavi, A., Oord, A., Vinyals, O.: Generating diverse high-fidelity images with vq-vae-2. Advances in neural information processing systems 32
2019
Cited alongside, same era.
Recht, B., Roelofs, R., Schmidt, L., Shankar, V.: Do imagenet classifiers generalize to imagenet? In: International Conference on Machine Learning, pp. 5389–5400 (2019). PMLR
2019
Cited alongside, same era.
Wang, H., Ge, S., Lipton, Z., Xing, E.P.: Learning robust global representations by penalizing local predictive power. Advances in Neural Information Processing Systems 32
2019
Cited alongside, same era.
Schuhmann, C., Beaumont, R., Vencu, R., Gordon, C.W., Wightman, R., Cherti, M., Coombes, T., Katta, A., Mullis, C., Wortsman, M., Schramowski, P., Kundurthy, S.R., Crowson, K., Schmidt, L., Kaczmarczyk, R., Jitsev, J.: LAION-5b: An open large-scale dataset for training next generation image-text models. In: Thirty-sixth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (2022). https://openreview.net/forum?id=M3Y74vmsMcY
2022
Later among the works it cites.
Kumar, A.: The illustrated image captioning using transformers. ankur3107.github.io (2022)
2022
Later among the works it cites.
Nichol, A.Q., Dhariwal, P., Ramesh, A., Shyam, P., Mishkin, P., Mcgrew, B., Sutskever, I., Chen, M.: GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. In: Proceedings of the 39th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 162, pp. 16784–16804 (2022). https://proceedings.mlr.press/v162/nichol22a.html
2022
Later among the works it cites.
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2019
Cited alongside, same era.
Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 6023–6032 (2019)
2019
Cited alongside, same era.
Tian, Y., Krishnan, D., Isola, P.: Contrastive multiview coding. In: Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XI 16, pp. 776–794 (2020). Springer
2020
Cited alongside, same era.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
You, Y., Li, J., Reddi, S., Hseu, J., Kumar, S., Bhojanapalli, S., Song, X., Demmel, J., Keutzer, K., Hsieh, C.-J.: Large batch optimization for deep learning: Training bert in 76 minutes. In: International Conference on Learning Representations (2020). https://openreview.net/forum?id=Syx4wnEtvH
2020
Cited alongside, same era.
Cubuk, E.D., Zoph, B., Shlens, J., Le, Q.V.: Randaugment: Practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 702–703 (2020)
2020
Cited alongside, same era.
Lei, S.: Understanding deep learning via large-scale systematic experiments. Master’s thesis, The University of Syndey (2021)
2021
Cited alongside, same era.
Hendrycks, D., Basart, S., Mu, N., Kadavath, S., Wang, F., Dorundo, E., Desai, R., Zhu, T., Parajuli, S., Guo, M., et al
2021
Cited alongside, same era.
Touvron, H., Cord, M., Jégou, H.: Deit iii: Revenge of the vit. In: European Conference on Computer Vision, pp. 516–533 (2022). Springer
2022
Later among the works it cites.
Lei, S., He, F., Yuan, Y., Tao, D.: Understanding deep learning via decision boundary. IEEE Transactions on Neural Networks and Learning Systems, 1–12 (2023) https://doi.org/10.1109/TNNLS.2023.3326654
2023
Closest in time.
2023
Closest in time.
He, R., Sun, S., Yu, X., Xue, C., Zhang, W., Torr, P., Bai, S., QI, X.: Is Synthetic Data From Generative Models Ready for Image Recognition? In: The Eleventh International Conference on Learning Representations (2023). https://openreview.net/forum?id=nUmCcZ5RKF
2023
Closest in time.
Sarıyıldız, M.B., Alahari, K., Larlus, D., Kalantidis, Y.: Fake it till you make it: Learning transferable representations from synthetic imagenet clones. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8011–8021 (2023)
2023
Closest in time.
2023
Closest in time.
Azizi, S., Kornblith, S., Saharia, C., Norouzi, M., Fleet, D.J.: Synthetic data from diffusion models improves imagenet classification. Transactions on Machine Learning Research (2023)
2023
Closest in time.
Kumari, N., Zhang, B., Zhang, R., Shechtman, E., Zhu, J.-Y.: Multi-concept customization of text-to-image diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 1931–1941 (2023)
2023
Closest in time.
2023
Closest in time.
Bansal, H., Grover, A.: Leaving reality to imagination: Robust classification via generated datasets. In: ICLR 2023 Workshop on Trustworthy and Reliable Large-Scale Machine Learning Models (2023). https://openreview.net/forum?id=LjGqAFP6rA
2023
Closest in time.
Li, J., Li, D., Savarese, S., Hoi, S.: Blip-2: Bootstrapping language-image pre-training with frozen image encoders and large language models. In: International Conference on Machine Learning, pp. 19730–19742 (2023). PMLR
2023
Closest in time.
Tian, Y., Fan, L., Isola, P., Chang, H., Krishnan, D.: Stablerep: Synthetic images from text-to-image models make strong visual representation learners. In: Thirty-seventh Conference on Neural Information Processing Systems (2023). https://openreview.net/forum?id=xpjsOQtKqx
2023
Closest in time.
Dunlap, L., Umino, A., Zhang, H., Yang, J., Gonzalez, J.E., Darrell, T.: Diversify your vision datasets with automatic diffusion-based augmentation. In: Thirty-seventh Conference on Neural Information Processing Systems (2023). https://openreview.net/forum?id=9wrYfqdrwk
2023
Closest in time.
Hao, Y., Chi, Z., Dong, L., Wei, F.: Optimizing prompts for text-to-image generation. In: Thirty-seventh Conference on Neural Information Processing Systems (2023). https://openreview.net/forum?id=BsZNWXD3a1
2023
Closest in time.
Chiang, W.-L., Li, Z., Lin, Z., Sheng, Y., Wu, Z., Zhang, H., Zheng, L., Zhuang, S., Zhuang, Y., Gonzalez, J.E., Stoica, I., Xing, E.P.: Vicuna: An Open-Source Chatbot Impressing GPT-4 with 90%* ChatGPT Quality (2023). https://lmsys.org/blog/2023-03-30-vicuna/
2023
Closest in time.
Smith, J.S., Hsu, Y.-C., Zhang, L., Hua, T., Kira, Z., Shen, Y., Jin, H.: Continual diffusion: Continual customization of text-to-image diffusion with c-loRA. Transactions on Machine Learning Research (2024)
2024
Closest in time.
Trabucco, B., Doherty, K., Gurinas, M.A., Salakhutdinov, R.: Effective data augmentation with diffusion models. In: The Twelfth International Conference on Learning Representations (2024). https://openreview.net/forum?id=ZWzUA9zeAg
2024
Closest in time.
Yuan, J., Pinto, F., Davies, A., Torr, P.: Not just pretty pictures: Toward interventional data augmentation using text-to-image generators. In: ICML 2024 Workshop on Foundation Models in the Wild (2024). https://openreview.net/forum?id=9Gja3UYcVg
2024
Closest in time.
Tian, Y., Fan, L., Chen, K., Katabi, D., Krishnan, D., Isola, P.: Learning vision from models rivals learning vision from data. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 15887–15898 (2024)
2024
Closest in time.
Hammoud, H.A.A.K., Itani, H., Pizzati, F., Bibi, A., Ghanem, B.: SynthCLIP: Are we ready for a fully synthetic CLIP training? In: Synthetic Data for Computer Vision Workshop @ CVPR 2024 (2024). https://openreview.net/forum?id=oKwYycMSrf
2024
Closest in time.