Object-Centric Image Generation with Factored Depths, Locations, and Appearances

The Hungarian method for the assignment problem

Kuhn, H. W · 1955

Earlier work this paper cites.

Generative adversarial nets

Goodfellow, I., Pouget-Abadle, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014

Earlier work this paper cites.

Auto-Encoding Variational Bayes

Kingma, D. P. and Welling, M · 2014

Earlier work this paper cites.

Stochastic backpropagation and approximate inference in deep generative models

Rezende, D. J., Mohamed, S., and Wierstra, D · 2014

Earlier work this paper cites.

Spatial transformer networks

Jaderberg, M., Simonyan, K., Zisserman, A., and Kavukcuoglu, K · 2015

Earlier work this paper cites.

Adam: A method for stochastic optimization

Kingma, D. P. and Ba, J. L · 2015

Earlier work this paper cites.

U-Net: Convolutional networks for biomedical image segmentation

Ronneberger, O., Fischer, P., and Brox, T · 2015

Earlier work this paper cites.

Attend, infer, repeat: Fast scene understanding with generative models

Eslami, S. M. A., Heess, N., Weber, T., Tassa, Y., Szepesvari, D., Kavukcuoglu, K., and Hinton, G. E · 2016

Earlier work this paper cites.

Tagger: Deep unsupervised perceptual grouping

Greff, K., Rasmus, A., Berglund, M., Hao, T. H., Schmidhuber, J., and Valpola, H · 2016

Earlier work this paper cites.

Rethinking the inception architecture for computer vision

Szegedy, C., Vanhoucke, V., Ioffe, S., and Shlens, J · 2016

Cited alongside, same era.

Variational inference: A review for statisticians

Blei, D. M., Kucukelbir, A., and McAuliffe, J. D · 2017

Cited alongside, same era.

Neural expectation maximization

Greff, K., van Steenkiste, S., and Schmidhuber, J · 2017

Cited alongside, same era.

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

Cited alongside, same era.

β \beta -VAE: Learning basic visual concepts with a constrained variational framework

Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2017

Cited alongside, same era.

Categorical reparameterization with Gumbel-Softmax

Jang, E., Gu, S., and Poole, B · 2017

Demystifying MMD GANs

Bińkowski, M., Sutherland, D. J., Arbel, M. N., and Gretton, A · 2018

Later among the works it cites.

Sequential attend, infer, repeat: Generative modelling of moving objects

Kosiorek, A. R., Kim, H., Posner, I., and Teh, Y. W · 2018

Later among the works it cites.

MONet: Unsupervised scene decomposition and representation

Original

Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., and Lerchner, A · 2019

Later among the works it cites.

Spatially invariant unsupervised object detection with convolutional neural networks

Crawford, E. and Pineau, J · 2019

Later among the works it cites.

Multi-object representation learning with iterative variational inference

Greff, K., Kaufmann, R. L., Kabra, R., Watters, N., Burgess, C., Zoran, D., Matthey, L., Botvinick, M., and Lerchner, A · 2019

Later among the works it cites.