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A range of methods with suitable inductive biases exist to learn interpretable object-centric representations of images without supervision.
Spatial Broadcast Decoder: A Simple Architecture for Learning Disentangled Representations in VAEs
Watters, N., Matthey, L., Burgess, C. P., and Lerchner, A · 1901
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Watters, N., Matthey, L., Bosnjak, M., Burgess, C. P., and Lerchner, A · 1905
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Deep Sparse Rectifier Neural Networks
Glorot, X., Bordes, A., and Bengio, Y · 2011
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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.
Efficient Inference in Occlusion-Aware Generative models of Images
Huang, J. and Murphy, K · 2015
Earlier work this paper cites.
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
Ioffe, S. and Szegedy, C · 2015
Earlier work this paper cites.
Spatial Transformer Networks
Jaderberg, M., Simonyan, K., Zisserman, A., et al · 2015
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Fast and Accurate Deep Network Learning by Exponential Linear Units (ELUs)
Clevert, D.-A., Unterthiner, T., and Hochreiter, S · 2016
Earlier work this paper cites.
Attend, Infer, Repeat: Fast Scene Understanding with Generative Models
Eslami, S. A., Heess, N., Weber, T., Tassa, Y., Szepesvari, D., Hinton, G. E., et al · 2016
Earlier work this paper cites.
Tagger: Deep Unsupervised Perceptual Grouping
Greff, K., Rasmus, A., Berglund, M., Hao, T., Valpola, H., and Schmidhuber, J · 2016
Earlier work this paper cites.
Se3-nets: Learning rigid body motion using deep neural networks
Byravan, A. and Fox, D · 2017
Earlier work this paper cites.
Language Modeling with Gated Convolutional Networks
Dauphin, Y. N., Fan, A., Auli, M., and Grangier, D · 2017
Cited alongside, same era.
Neural Expectation Maximization
Greff, K., van Steenkiste, S., and Schmidhuber, J · 2017
Cited alongside, same era.
Opening the Black Box of Deep Neural Networks via Information
Shwartz-Ziv, R. and Tishby, N · 2017
Cited alongside, same era.
Sylvester Normalizing Flows for Variational Inference
Berg, R. v. d., Hasenclever, L., Tomczak, J. M., and Welling, M · 2018
Cited alongside, same era.
ShapeStacks: Learning Vision-Based Physical Intuition for Generalised Object Stacking
Groth, O., Fuchs, F. B., Posner, I., and Vedaldi, A · 2018
Cited alongside, same era.
Sequential Attend, Infer, Repeat: Generative Modelling of Moving Objects
Spatially Invariant Unsupervised Object Detection with Convolutional Neural Networks
Crawford, E. and Pineau, J · 2019
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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
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Multi-Object Datasets
Kabra, R., Burgess, C., Matthey, L., Kaufman, R. L., Greff, K., Reynolds, M., and Lerchner, A · 2019
Later among the works it cites.
Stacked Capsule Autoencoders
Kosiorek, A. R., Sabour, S., Teh, Y. W., and Hinton, G. E · 2019
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GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Engelcke, M., Kosiorek, A. R., Parker Jones, O., and Posner, I · 2020
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SCALOR: Generative World Models with Scalable Object Representations
Jiang, J., Janghorbani, S., De Melo, G., and Ahn, S · 2020
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Kosiorek, A., Kim, H., Teh, Y. W., and Posner, I · 2018
Cited alongside, same era.
Rezende, D. J. and Viola, F · 2018
Cited alongside, same era.
Object Discovery with a Copy-Pasting GAN
Arandjelović, R. and Zisserman, A · 2019
Cited alongside, same era.
Compositional GAN: Learning Image-Conditional Binary Composition
Azadi, S., Pathak, D., Ebrahimi, S., and Darrell, T · 2019
Cited alongside, same era.
Emergence of Object Segmentation in Perturbed Generative Models
Bielski, A. and Favaro, P · 2019
Cited alongside, same era.
MONet: Unsupervised Scene Decomposition and Representation
Burgess, C. P., Matthey, L., Watters, N., Kabra, R., Higgins, I., Botvinick, M., and Lerchner, A · 2019
Cited alongside, same era.
Unsupervised Object Segmentation by Redrawing
Chen, M., Artières, T., and Denoyer, L · 2019
Cited alongside, same era.
Closest in time.
Contrastive Learning of Structured World Models
Kipf, T., van der Pol, E., and Welling, M · 2020
Closest in time.
Structured object-aware physics prediction for video modeling and planning
Kossen, J., Stelzner, K., Hussing, M., Voelcker, C., and Kersting, K · 2020
Closest in time.
SPACE: Unsupervised Object-Oriented Scene Representation via Spatial Attention and Decomposition
Lin, Z., Wu, Y.-F., Peri, S. V., Sun, W., Singh, G., Deng, F., Jiang, J., and Ahn, S · 2020
Closest in time.
BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images
Nguyen-Phuoc, T., Richardt, C., Mai, L., Yang, Y.-L., and Mitra, N · 2020
Closest in time.
Towards causal generative scene models via competition of experts
von Kügelgen, J., Ustyuzhaninov, I., Gehler, P., Bethge, M., and Schölkopf, B · 2020
Closest in time.