Self-Guided Generation of Minority Samples Using Diffusion Models

Robbins, H.E.: An empirical bayes approach to statistics. In: Breakthroughs in statistics, pp. 388–394. Springer (1992)

1992

Earlier work this paper cites.

Breunig, M.M., Kriegel, H.P., Ng, R.T., Sander, J.: Lof: identifying density-based local outliers. In: Proceedings of the 2000 ACM SIGMOD international conference on Management of data. pp. 93–104 (2000)

2000

Earlier work this paper cites.

Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)

2009

Earlier work this paper cites.

Vincent, P.: A connection between score matching and denoising autoencoders. Neural computation 23

2011

Earlier work this paper cites.

Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems 25

2012

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. Advances in neural information processing systems 27

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. Springer (2014)

2014

Earlier work this paper cites.

Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (ICCV) (December 2015)

2015

Earlier work this paper cites.

Sohl-Dickstein, J., Weiss, E., Maheswaranathan, N., Ganguli, S.: Deep unsupervised learning using nonequilibrium thermodynamics. In: International Conference on Machine Learning. pp. 2256–2265. PMLR (2015)

2015

Earlier work this paper cites.

Yu, F., Seff, A., Zhang, Y., Song, S., Funkhouser, T., Xiao, J.: Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop. arXiv preprint arXiv:1506.03365 (2015)

Original

2015

Earlier work this paper cites.

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems 30

2017

Earlier work this paper cites.

Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis. arXiv preprint arXiv:1809.11096 (2018)

Original

2018

Earlier work this paper cites.

Zhang, R., Isola, P., Efros, A.A., Shechtman, E., Wang, O.: The unreasonable effectiveness of deep features as a perceptual metric. In: CVPR (2018)

2018

Earlier work this paper cites.

Amini, A., Soleimany, A.P., Schwarting, W., Bhatia, S.N., Rus, D.: Uncovering and mitigating algorithmic bias through learned latent structure. In: Proceedings of the 2019 AAAI/ACM Conference on AI, Ethics, and Society. pp. 289–295 (2019)

2019

Earlier work this paper cites.

Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4401–4410 (2019)

2019

Earlier work this paper cites.

Kynkäänniemi, T., Karras, T., Laine, S., Lehtinen, J., Aila, T.: Improved precision and recall metric for assessing generative models. Advances in Neural Information Processing Systems 32

2019

Earlier work this paper cites.

Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., et al.: Pytorch: An imperative style, high-performance deep learning library. Advances in neural information processing systems 32

2019

Earlier work this paper cites.

Ravuri, S., Vinyals, O.: Classification accuracy score for conditional generative models. Advances in neural information processing systems 32

2019

Earlier work this paper cites.

Serrà, J., Álvarez, D., Gómez, V., Slizovskaia, O., Núñez, J.F., Luque, J.: Input complexity and out-of-distribution detection with likelihood-based generative models. arXiv preprint arXiv:1909.11480 (2019)

Original

2019

Earlier work this paper cites.

Song, Y., Ermon, S.: Generative modeling by estimating gradients of the data distribution. Advances in Neural Information Processing Systems 32

2019

Earlier work this paper cites.

Zhao, Y., Nasrullah, Z., Li, Z.: Pyod: A python toolbox for scalable outlier detection. Journal of Machine Learning Research 20

2019

Cited alongside, same era.

Choi, K., Grover, A., Singh, T., Shu, R., Ermon, S.: Fair generative modeling via weak supervision. In: Proceedings of the 37th International Conference on Machine Learning (ICML). PMLR (2020)

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.

Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. In: Proc. NeurIPS (2020)

2020

Cited alongside, same era.

Song, J., Meng, C., Ermon, S.: Denoising diffusion implicit models. arXiv preprint arXiv:2010.02502 (2020)

Original

Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)

Original

2022

Later among the works it cites.

Karras, T., Aittala, M., Aila, T., Laine, S.: Elucidating the design space of diffusion-based generative models. Advances in Neural Information Processing Systems 35

2022

Later among the works it cites.

Lin, Z., Liang, H., Fanti, G., Sekar, V., Sharma, R.A., Soltanaghaei, E., Rowe, A., Namkung, H., Liu, Z., Kim, D., et al.: Raregan: Generating samples for rare classes. arXiv preprint arXiv:2203.10674 (2022)

Original

2022

Later among the works it cites.

Parmar, G., Zhang, R., Zhu, J.Y.: On aliased resizing and surprising subtleties in gan evaluation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 11410–11420 (2022)

2022

Later among the works it cites.