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We propose TopDis (Topological Disentanglement), a method for learning disentangled representations via adding a multi-scale topological loss term.
The framed Morse complex and its invariants
Barannikov, S · 1994
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Groupoids: unifying internal and external symmetry
Weinstein, A · 1996
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A 3d face model for pose and illumination invariant face recognition
Paysan, P., Knothe, R., Amberg, B., Romdhani, S., and Vetter, T · 2009
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Disentangling factors of variation via generative entangling
Desjardins, G., Courville, A., and Bengio, Y · 2012
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Representation learning: A review and new perspectives
Bengio, Y., Courville, A., and Vincent, P · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Learning the irreducible representations of commutative lie groups
Cohen, T. and Welling, M · 2014
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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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Semi-supervised learning with deep generative models
Kingma, D. P., Mohamed, S., Jimenez Rezende, D., and Welling, M · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2015
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Deep convolutional inverse graphics network
Kulkarni, T. D., Whitney, W. F., Kohli, P., and Tenenbaum, J · 2015
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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Deep learning , volume 1
Goodfellow, I., Bengio, Y., Courville, A., and Bengio, Y · 2016
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Disentangling factors of variation in deep representation using adversarial training
Mathieu, M. F., Zhao, J. J., Zhao, J., Ramesh, A., Sprechmann, P., and LeCun, Y · 2016
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Understanding disentangling in β \beta -vae
Burgess, C. P., Higgins, I., Pal, A., Matthey, L., Watters, N., Desjardins, G., and Lerchner, A · 2017
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An introduction to topological data analysis: fundamental and practical aspects for data scientists
Chazal, F. and Michel, B · 2017
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Unsupervised learning of disentangled representations from video
Denton, E. L. et al · 2017
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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
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dsprites: Disentanglement testing sprites dataset
Matthey, L., Higgins, I., Hassabis, D., and Lerchner, A · 2017
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Learning disentangled representations with semi-supervised deep generative models
Paige, B., van de Meent, J.-W., Desmaison, A., Goodman, N., Kohli, P., Wood, F., Torr, P., et al · 2017
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From deep learning of disentangled representations to higher-level cognition, 2018
Bengio, Y · 2018
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3d shapes dataset
Burgess, C. and Kim, H · 2018
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Isolating sources of disentanglement in variational autoencoders
Chen, R. T., Li, X., Grosse, R. B., and Duvenaud, D. K · 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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Dual swap disentangling
Feng, Z., Wang, X., Ke, C., Zeng, A., Tao, D., and Song, M · 2018
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Image-to-image translation for cross-domain disentanglement
Gonzalez-Garcia, A., van de Weijer, J., and Bengio, Y · 2018
Cited alongside, same era.
Towards a definition of disentangled representations
Higgins, I., Amos, D., Pfau, D., Racaniere, S., Matthey, L., Rezende, D., and Lerchner, A · 2018
A sober look at the unsupervised learning of disentangled representations and their evaluation
Locatello, F., Bauer, S., Lucic, M., Rätsch, G., Gelly, S., Schölkopf, B., and Bachem, O · 2020
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Topological autoencoders
Moor, M., Horn, M., Rieck, B., and Borgwardt, K · 2020
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The hessian penalty: A weak prior for unsupervised disentanglement
Peebles, W., Peebles, J., Zhu, J.-Y., Efros, A., and Torralba, A · 2020
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Controlvae: Controllable variational autoencoder
Shao, H., Yao, S., Sun, D., Zhang, A., Liu, S., Liu, D., Wang, J., and Abdelzaher, T · 2020
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Gradient surgery for multi-task learning
Yu, T., Kumar, S., Gupta, A., Levine, S., Hausman, K., and Finn, C · 2020
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Joint disentangling and adaptation for cross-domain person re-identification
Zou, Y., Yang, X., Yu, Z., Kumar, B., and Kautz, J · 2020
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Disentangling by factorising
Kim, H. and Mnih, A · 2018
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Variational inference of disentangled latent concepts from unlabeled observations
Kumar, A., Sattigeri, P., and Balakrishnan, A · 2018
Cited alongside, same era.
Learning deep disentangled embeddings with the f-statistic loss
Ridgeway, K. and Mozer, M. C · 2018
Cited alongside, same era.
Geometry of deep generative models for disentangled representations
Shukla, A., Uppal, S., Bhagat, S., Anand, S., and Turaga, P · 2018
Cited alongside, same era.
Interpretable convolutional neural networks
Zhang, Q., Wu, Y. N., and Zhu, S.-C · 2018
Cited alongside, same era.
Interpreting deep visual representations via network dissection
Zhou, B., Bau, D., Oliva, A., and Torralba, A · 2018
Cited alongside, same era.
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On the transfer of disentangled representations in realistic settings
Dittadi, A., Trauble, F., Locatello, F., Wuthrich, M., Agrawal, V., Winther, O., Bauer, S., and Scholkopf, B · 2021
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Learning disentangled representations with the wasserstein autoencoder
Gaujac, B., Feige, I., and Barber, D · 2021
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A framework for differential calculus on persistence barcodes
Leygonie, J., Oudot, S., and Tillmann, U · 2021
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Group-disentangled representation learning with weakly-supervised regularization
Tran, L., Khasahmadi, A. H., Sanghi, A., and Asgari, S · 2021
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On disentangled representations learned from correlated data
Träuble, F., Creager, E., Kilbertus, N., Locatello, F., Dittadi, A., Goyal, A., Schölkopf, B., and Bauer, S · 2021
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The geometry of deep generative image models and its applications
Wang, B. and Ponce, C. R · 2021
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Orthogonal jacobian regularization for unsupervised disentanglement in image generation
Wei, Y., Shi, Y., Liu, X., Ji, Z., Gao, Y., Wu, Z., and Zuo, W · 2021
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Towards building a group-based unsupervised representation disentanglement framework
Yang, T., Ren, X., Wang, Y., Zeng, W., and Zheng, N · 2021
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Evaluating the disentanglement of deep generative models through manifold topology
Zhou, S., Zelikman, E., Lu, F., Ng, A. Y., Carlsson, G. E., and Ermon, S · 2021
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Representation Topology Divergence: A method for comparing neural network representations
Barannikov, S., Trofimov, I., Balabin, N., and Burnaev, E · 2022
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Measuring disentanglement: A review of metrics
Zaidi, J., Boilard, J., Gagnon, G., and Carbonneau, M.-A · 2022
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Explaining, evaluating and enhancing neural networks’ learned representations
Bertolini, M., Clevert, D.-A., and Montanari, F · 2023
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Dava: Disentangling adversarial variational autoencoder
Estermann, B. and Wattenhofer, R · 2023
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Overlooked implications of the reconstruction loss for vae disentanglement
Michlo, N., Klein, R., and James, S · 2023
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Disentanglement of correlated factors via hausdorff factorized support
Roth, K., Ibrahim, M., Akata, Z., Vincent, P., and Bouchacourt, D · 2023
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Learning topology-preserving data representations
Trofimov, I., Cherniavskii, D., Tulchinskii, E., Balabin, N., Burnaev, E., and Barannikov, S · 2023
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