How to Not Measure Disentanglement

Reducing the dimensionality of data with neural networks

Hinton, G. E. and Salakhutdinov, R. R · 2006

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Extracting and composing robust features with denoising autoencoders

Vincent, P., Larochelle, H., Bengio, Y., and Manzagol, P.-A · 2008

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Learning to detect unseen object classes by between-class attribute transfer

Lampert, C. H., Nickisch, H., and Harmeling, S · 2009

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A survey on transfer learning

Pan, S. J., Yang, Q., et al · 2010

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Safety in numbers: Learning categories from few examples with multi model knowledge transfer

Tommasi, T., Orabona, F., and Caputo, B · 2010

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Representation learning: A review and new perspectives

Bengio, Y., Courville, A., and Vincent, P · 2013

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Coupled dictionary and feature space learning with applications to cross-domain image synthesis and recognition

Huang, D.-A. and Frank Wang, Y.-C · 2013

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Intriguing properties of neural networks

Original

Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R · 2013

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Generative adversarial nets

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

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Adversarial autoencoders

Original

Makhzani, A., Shlens, J., Jaitly, N., Goodfellow, I., and Frey, B · 2015

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Deep neural networks are easily fooled: High confidence predictions for unrecognizable images

Nguyen, A., Yosinski, J., and Clune, J · 2015

Cited alongside, same era.

Deep visual analogy-making

Reed, S. E., Zhang, Y., Zhang, Y., and Lee, H · 2015

Cited alongside, same era.

An embarrassingly simple approach to zero-shot learning

Romera-Paredes, B. and Torr, P · 2015

Cited alongside, same era.

Unsupervised learning of disentangled representations from video

Denton, E. L. et al · 2017

Cited alongside, same era.

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.

Disentangling factors of variation by mixing them

Original

Isolating sources of disentanglement in variational autoencoders

Original

Chen, T. Q., Li, X., Grosse, R., and Duvenaud, D · 2018

Later among the works it cites.

A framework for the quantitative evaluation of disentangled representations

Eastwood, C. and Williams, C. K · 2018

Later among the works it cites.

Towards a definition of disentangled representations

Original

Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A · 2018

Later among the works it cites.

Disentangling by factorising

Original

Kim, H. and Mnih, A · 2018

Later among the works it cites.

Challenging common assumptions in the unsupervised learning of disentangled representations

Original

Locatello, F., Bauer, S., Lucic, M., Gelly, S., Schölkopf, B., and Bachem, O · 2018