Compositional Visual Generation with Composable Diffusion Models

Chomsky, N.: Aspects of the Theory of Syntax. The MIT Press, Cambridge (1965), http://www.amazon.com/Aspects-Theory-Syntax-Noam-Chomsky/dp/0262530074

1965

Earlier work this paper cites.

Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. ACM siggraph computer graphics 21

1987

Earlier work this paper cites.

Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural computation 14

2002

Earlier work this paper cites.

LeCun, Y., Chopra, S., Hadsell, R., Ranzato, M., Huang, F.: A tutorial on energy-based learning. Predicting structured data 1

2006

Earlier work this paper cites.

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

2011

Earlier work this paper cites.

Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980 (2014)

Original

2014

Earlier work this paper cites.

Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350

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.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2818–2826 (2016)

2016

Earlier work this paper cites.

Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (2017)

2017

Earlier work this paper cites.

Johnson, J., Hariharan, B., Van Der Maaten, L., Fei-Fei, L., Lawrence Zitnick, C., Girshick, R.: Clevr: A diagnostic dataset for compositional language and elementary visual reasoning. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2901–2910 (2017)

2017

Earlier work this paper cites.

Karras, T., Aila, T., Laine, S., Lehtinen, J.: Progressive growing of gans for improved quality, stability, and variation. arXiv preprint arXiv:1710.10196 (2017)

Original

2017

Earlier work this paper cites.

Loshchilov, I., Hutter, F.: Decoupled weight decay regularization. arXiv preprint arXiv:1711.05101 (2017)

Original

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.

Xiao, T., Hong, J., Ma, J.: Elegant: Exchanging latent encodings with gan for transferring multiple face attributes. In: Proceedings of the European conference on computer vision (ECCV). pp. 168–184 (2018)

2018

Earlier work this paper cites.

Xu, T., Zhang, P., Huang, Q., Zhang, H., Gan, Z., Huang, X., He, X.: Attngan: Fine-grained text to image generation with attentional generative adversarial networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 1316–1324 (2018)

2018

Earlier work this paper cites.

Du, Y., Mordatch, I.: Implicit generation and generalization in energy-based models. arXiv preprint arXiv:1903.08689 (2019)

Original

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.

Nijkamp, E., Hill, M., Han, T., Zhu, S.C., Wu, Y.N.: On the anatomy of mcmc-based maximum likelihood learning of energy-based models. arXiv preprint arXiv:1903.12370 (2019)

Original

2019

Earlier work this paper cites.

Chen, N., Zhang, Y., Zen, H., Weiss, R.J., Norouzi, M., Chan, W.: Wavegrad: Estimating gradients for waveform generation. arXiv preprint arXiv:2009.00713 (2020)

Original

2020

Cited alongside, same era.

DCGM: Gender, age, and emotions extracted for flickr-faces-hq dataset (ffhq). https://github.com/DCGM/ffhq-features-dataset (2020)

2020

Cited alongside, same era.

Du, Y., Li, S., Mordatch, I.: Compositional visual generation with energy based models. Advances in Neural Information Processing Systems 33

2020

Cited alongside, same era.

Du, Y., Li, S., Tenenbaum, J., Mordatch, I.: Improved contrastive divergence training of energy based models. arXiv preprint arXiv:2012.01316 (2020)

Original

2020

Cited alongside, same era.

Grathwohl, W., Wang, K.C., Jacobsen, J.H., Duvenaud, D., Zemel, R.: Learning the stein discrepancy for training and evaluating energy-based models without sampling. In: International Conference on Machine Learning (2020)

Nichol, A.Q., Dhariwal, P.: Improved denoising diffusion probabilistic models. In: International Conference on Machine Learning. pp. 8162–8171. PMLR (2021)

2021

Later among the works it cites.

Nie, W., Vahdat, A., Anandkumar, A.: Controllable and compositional generation with latent-space energy-based models. Advances in Neural Information Processing Systems 34

2021

Later among the works it cites.

Ramesh, A., Pavlov, M., Goh, G., Gray, S., Voss, C., Radford, A., Chen, M., Sutskever, I.: Zero-shot text-to-image generation. In: International Conference on Machine Learning. pp. 8821–8831. PMLR (2021)

2021

Later among the works it cites.

Rombach, R., Blattmann, A., Lorenz, D., Esser, P., Ommer, B.: High-resolution image synthesis with latent diffusion models (2021)

2021

Later among the works it cites.

Saharia, C., Chan, W., Chang, H., Lee, C.A., Ho, J., Salimans, T., Fleet, D.J., Norouzi, M.: Palette: Image-to-image diffusion models. arXiv preprint arXiv:2111.05826 (2021)