PCA-AE: Principal Component Analysis Autoencoder for Organising the Latent Space of Generative Networks

Kingma, D.P.,Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2014)

Van den Aaron, O., Nal, K., Lasse, E., Oriol, V., Alex, G., et al.: Conditional image generation with pixelcnn decoders. In: Advances in Neural Information Processing Systems, pp. 4790–4798 (2016)

Casper, K., Sønderby, T., Raiko, L., Maaløe, S., Kaae, S., Ole, W.: Ladder variational autoencoders. In: Advances in Neural Information Processing Systems, pp. 3738–3746 (2016)

Ilya, T., Olivier, B., Sylvain, G., Bernhard, S.: Wasserstein auto-encoders. In: International Conference on Learning Representations (2018)

Huaibo, H., Ran, H., Zhenan, S., Tieniu, T., et al.: Introvae: introspective variational autoencoders for photographic image synthesis. In: Advances in Neural Information Processing Systems, pp. 52–63 (2018)

Ari, H., Arno, S., Juho, K.: Towards photographic image manipulation with balanced growing of generative autoencoders. In The IEEE Winter Conference on Applications of Computer Vision, pp. 3120–3129 (2020)

Ian, G., Jean, P.-A., Mehdi, M., Bing, X., David, W.-F., Sherjil, O., Aaron, C., Yoshua, B.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

Tim, S., Ian, G., Wojciech, Zaremba, V., Cheung, A., Radford, X., Chen: Improved techniques for training gans. In: Advances in Neural Information Processing Systems, pp. 2234–2242 (2016)

Tero, Karras, Timo, Aila, Samuli, Laine, Jaakko, Lehtinen: Progressive growing of gans for improved quality, stability, and variation. In International Conference on Learning Representations, 2018

Akash, S., Lazar, V., Chris, R., Michael, U., Gutmann, C., Sutton: Veegan: reducing mode collapse in gans using implicit variational learning. In: Advances in Neural Information Processing Systems, pp. 3308–3318 (2017)

Tero, K., Samuli, L., Timo, A.: A style-based generator architecture for generative adversarial networks. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2019)

Yunjey, C., Minje, C., Munyoung, K., Jung-Woo, H., Sunghun, K., Jaegul, C.: Stargan: unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (2018)

Jun-Yan, Z., Richard, Z., Deepak, P., Trevor, D., Alexei, A., Efros, O., Wang, E., Shechtman: Toward multimodal image-to-image translation. In: Advances in Neural Information Processing Systems, pp. 465–476 (2017)

Chi-Hieu, P., Carlos, T., Hélène, M., Nathalie, B., Ronan, F., Nicolas, P., François, R.: Simultaneous super-resolution and segmentation using a generative adversarial network: Application to neonatal brain MRI. In: International Symposium on Biomedical Imaging, pp. 991–994 (2019)

Durk, P., Kingma, S., Mohamed, D., Jimenez, R., Max, W.: Semi-supervised learning with deep generative models. In: Advances in Neural Information Processing Systems, pp. 3581–3589 (2014)

Scott, R., Kihyuk, S., Yuting, Z., Honglak, L.: Learning to disentangle factors of variation with manifold interaction. In: International Conference on Machine Learning, pp. 1431–1439 (2014)

Michael, F., Mathieu, J., Jake, Z., Junbo, Z., Aditya, R., Pablo, S., Yann, L.: Disentangling factors of variation in deep representation using adversarial training. In: Advances in Neural Information Processing Systems, pp. 5040–5048 (2016)

Emily, L., Denton, et al.: Unsupervised learning of disentangled representations from video. In: Advances in Neural Information Processing Systems, pp. 4414–4423 (2017)

Wei-Ning, H., Yu, Z., James, G.: Unsupervised learning of disentangled and interpretable representations from sequential data. In: Advances in Neural Information Processing Systems, pp. 1878–1889 (2017)

Narayanaswamy, S., Brooks, P., Jan-Willem, Van de, M., Alban, D., Noah, G., Pushmeet, K., Frank, Wood, Philip, Tood: Learning disentangled representations with semi-supervised deep generative models. In: Advances in Neural Information Processing Systems, pp. 5925–5935 (2017)

Marc’Aurelio, R., Y-Lan, B., Yann, L., Cun: Sparse feature learning for deep belief networks. In: Advances in Neural Information Processing Systems, pp. 1185–1192 (2008)

Xavier, Glorot, A., Bordes, Y.: Bengio: deep sparse rectifier neural networks. In: Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics (2011)

Alireza, M., Brendan, F.: K-sparse autoencoders. In: International Conference on Learning Representations (2014)

Alec, R., Luke, M., Soumith, C.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Learning Representations (2016)

Martin, A., Soumith, C., Léon, B.: Wasserstein generative adversarial networks. In: International Conference on Machine Learning, pp. 214–223 (2017)

Ishaan, G., Faruk, A., Martin, A., Vincent, D., Aaron, C.: Courville: improved training of wasserstein gans. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)

Xi, C., Yan, D., Rein, H., John, S., Ilya, S., Pieter, A.: Infogan: interpretable representation learning by information maximizing generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2172–2180 (2016)

Augustus, O., Christopher, O., Jonathon, S.: Conditional image synthesis with auxiliary classifier gans. In: Proceedings of the 34th International Conference on Machine Learning-Volume 70, pp. 2642–2651. JMLR. org (2017)

Xinchen, Y., Jimei, Y., Kihyuk, S., Honglak, L.: Attribute2image: conditional image generation from visual attributes. In: European Conference on Computer Vision, pp. 776–791. Springer (2016)

Sudipto, M., Himanshu, A., Eugene, L., Sreeram, K.: Clustergan: latent space clustering in generative adversarial networks. In: Proceedings of the AAAI Conference on Artificial Intelligence 33, pp. 4610–4617 (2019)

Quentin, D., Chi-Hieu, P., Clément, C., Carlos, T., Guillaume, D., Hélène, M., Nathalie, B., Ronan, F., Nicolas, P., François, R.: SegSRGAN: super-resolution and segmentation uing generative adversarial networks $$-$$ application to neonatal brain MRI. Comput. Biol. Med., 103755 (2020)

Salah, R., Yoshua, B., Aaron, C., Pascal, V., Mehdi, M.: Disentangling factors of variation for facial expression recognition. In: European Conference on Computer Vision, pp. 808–822. Springer (2012)

Brian, C., Jesse, A., Livezey, A.K., Bansal, B.A.: Olshausen: discovering hidden factors of variation in deep networks. arXiv:1412.6583 (2014)

Abhishek, K., Prasanna, S., Avinash, B.: Variational inference of disentangled latent concepts from unlabeled observations. In: International Conference on Learning Representations (2018)

José, L.: Overcoming the disentanglement vs reconstruction trade-off via jacobian supervision. In: International Conference on Learning Representations (2019)

Guillaume, L., Neil, Z., Nicolas, U., Antoine, B., Ludovic, D., et al.: Fader networks: manipulating images by sliding attributes. In: Advances in Neural Information Processing Systems, pp. 5967–5976 (2017)

Irina, H., Loic, M., Arka, P., Christopher, B., Xavier, G., Matthew, B., Shakir, M., Alexander, L.: $$\beta $$-vae: learning basic visual concepts with a constrained variational framework. In: International Conference on Learning Representations, vol. 2, p. 6 (2017)

Christopher, P., Burgess, I., Higgins, A., Pal, L., Matthey, N., Watters, G., Desjardins, A., Lerchner.: Understanding disentangling in $$\beta $$-vae. In: NIPS Workshop on Learning Disentangled Representations (2018)

Hyunjik, K., Andriy, M.: Disentangling by factorising. In: International Conference on Machine Learning (2018)

Tian, Q., Chen, X., Li, R.B, Grosse, D.K., Duvenaud.: Isolating sources of disentanglement in variational autoencoders. In: Advances in Neural Information Processing Systems, pp. 2610–2620 (2018)

Wei, W., Dan, Y., Feiyu, C., Yunsheng, P., Sheng, H., Yongxin, G.: Clustering with orthogonal autoencoder. IEEE. Access 7, 62421–62432 (2019)

Yaodong, Y., Kwan, H.R., Chan, C., You, C., Song, Y.M.: Learning diverse and discriminative representations via the principle of maximal coding rate reduction. Advances in Neural Information Processing Systems, 33 (2020)

Sergey, I., Christian, S.: Batch normalization: accelerating deep network training by reducing internal covariate shift. In: International Conference on Machine Learning, pp. 448–456 (2015)

Diederik, K., Jimmy, B.: Adam: a method for stochastic optimization. In: International Conference on Learning Representations (2015)

Ziwei, L., Ping, L., Xiaogang, W., Xiaoou, T.: Deep learning face attributes in the wild. In: Proceedings of International Conference on Computer Vision (2015)

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