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Unsupervised extraction of objects from low-level visual data is an important goal for further progress in machine learning.
Exploiting spatial invariance for scalable unsupervised object tracking
Crawford, E. and Pineau, J · 1911
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Causality
Pearl, J · 2009
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Interaction networks for learning about objects, relations and physics
Battaglia, P., Pascanu, R., Lai, M., Rezende, D. J., et al · 2016
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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
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Tagger: Deep unsupervised perceptual grouping
Greff, K., Rasmus, A., Berglund, M., Hao, T., Valpola, H., and Schmidhuber, J · 2016
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Shuffle and learn: unsupervised learning using temporal order verification
Misra, I., Zitnick, C. L., and Hebert, M · 2016
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You only look once: Unified, real-time object detection
Redmon, J., Divvala, S., Girshick, R., and Farhadi, A · 2016
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Neural expectation maximization
Greff, K., Van Steenkiste, S., and Schmidhuber, J · 2017
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Nonlinear ica of temporally dependent stationary sources
Hyvarinen, A. and Morioka, H · 2017
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The multi-entity variational autoencoder
Nash, C., Eslami, S. A., Burgess, C., Higgins, I., Zoran, D., Weber, T., and Battaglia, P · 2017
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Playing hard exploration games by watching youtube
Aytar, Y., Pfaff, T., Budden, D., Paine, T. L., Wang, Z., and de Freitas, N · 2018
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A framework for the quantitative evaluation of disentangled representations
Eastwood, C. and Williams, C. K · 2018
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Unsupervised intuitive physics from visual observations
Ehrhardt, S., Monszpart, A., Mitra, N., and Vedaldi, A · 2018
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Learning deep representations by mutual information estimation and maximization
Hjelm, R. D., Fedorov, A., Lavoie-Marchildon, S., Grewal, K., Bachman, P., Trischler, A., and Bengio, Y · 2018
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Unsupervised learning of object landmarks through conditional image generation
Jakab, T., Gupta, A., Bilen, H., and Vedaldi, A · 2018
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Sequential attend, infer, repeat: Generative modelling of moving objects
Kosiorek, A., Kim, H., Teh, Y. W., and Posner, I · 2018
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Challenging common assumptions in the unsupervised learning of disentangled representations
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2018
Cited alongside, same era.
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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Momentum contrast for unsupervised visual representation learning
He, K., Fan, H., Wu, Y., Xie, S., and Girshick, R · 2019
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Data-efficient image recognition with contrastive predictive coding
Hénaff, O. J., Srinivas, A., De Fauw, J., Razavi, A., Doersch, C., Eslami, S., and Oord, A. v. d · 2019
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Scalable object-oriented sequential generative models
Jiang, J., Janghorbani, S., de Melo, G., and Ahn, S · 2019
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Oord, A. v. d., Li, Y., and Vinyals, O · 2018
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Time-contrastive networks: Self-supervised learning from video
Sermanet, P., Lynch, C., Chebotar, Y., Hsu, J., Jang, E., Schaal, S., Levine, S., and Brain, G · 2018
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Relational neural expectation maximization: Unsupervised discovery of objects and their interactions
Van Steenkiste, S., Chang, M., Greff, K., and Schmidhuber, J · 2018
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Unsupervised discovery of object landmarks as structural representations
Zhang, Y., Guo, Y., Jin, Y., Luo, Y., He, Z., and Lee, H · 2018
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Unsupervised state representation learning in atari
Anand, A., Racah, E., Ozair, S., Bengio, Y., Côté, M.-A., and Hjelm, R. D · 2019
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A theoretical analysis of contrastive unsupervised representation learning
Arora, S., Khandeparkar, H., Khodak, M., Plevrakis, O., and Saunshi, N · 2019
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Learning representations by maximizing mutual information across views
Bachman, P., Hjelm, R. D., and Buchwalter, W · 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
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Kipf, T., van der Pol, E., and Welling, M · 2019
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Structured object-aware physics prediction for video modeling and planning
Kossen, J., Stelzner, K., Hussing, M., Voelcker, C., and Kersting, K · 2019
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Unsupervised learning of object keypoints for perception and control
Kulkarni, T. D., Gupta, A., Ionescu, C., Borgeaud, S., Reynolds, M., Zisserman, A., and Mnih, V · 2019
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Unsupervised learning of object structure and dynamics from videos
Minderer, M., Sun, C., Villegas, R., Cole, F., Murphy, K. P., and Lee, H · 2019
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Self-supervised representation learning
Weng, L · 2019
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Contrastive self-supervised learning, 2020
Anand, A · 2020
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A simple framework for contrastive learning of visual representations
Chen, T., Kornblith, S., Norouzi, M., and Hinton, G · 2020
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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
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Learning to simulate complex physics with graph networks
Sanchez-Gonzalez, A., Godwin, J., Pfaff, T., Ying, R., Leskovec, J., and Battaglia, P. W · 2020
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