Disentangle Datasets

  • CelebA [14] (dataset for human faces): [12, 2, 11, 17, 13, 8, 13, 18]
  • MNIST [10], MNIST-M [4] (digits): [16, 15, 12, 5, 2, 11, 9, 17, 6, 8, 13, 3]
  • Yosemite [19] (summer and winter scenes): [11]
  • Artworks [19] (Monet and Van Gogh): [11]
  • 2D Sprites (game characters): [15, 9, 6, 8, 3]
  • LineMod [7] (3D object): [9]
  • 11k Hands [1] (hand gestures): [17]

Reference

[1] M. Afifi. Gender recognition and biometric identification using a large dataset of hand images. arXiv preprint arXiv:1711.04322, 2017.

[2] E. Dupont. Learning disentangled joint continuous and discrete representations. In S. Bengio, H.Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31, pages 708–718. Curran Associates, Inc., 2018.

[3] Z. Feng, X. Wang, C. Ke, A.-X. Zeng, D. Tao, and M. Song. Dual swap disentangling. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31, pages 5898–5908. Curran Associates, Inc., 2018.

[4] Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky. Domain-adversarial training of neural networks. The Journal of Machine Learning Research, 17(1):2096–2030, 2016.

[5] A. Gonzalez-Garcia, J. van de Weijer, and Y. Bengio. Image-to-image translation for cross-domain disentanglement. In S. Bengio, H. Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31, pages 1294–1305. Curran Associates, Inc., 2018.

[6] N. Hadad, L. Wolf, and M. Shahar. A two-step disentanglement method. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.

[7] S. Hinterstoisser, V. Lepetit, S. Ilic, S. Holzer, G. Bradski, K. Konolige, and N. Navab. Model based training, detection and pose estimation of texture-less 3d objects in heavily cluttered scenes. In Asian conference on computer vision, pages 548–562. Springer, 2012.

[8] Q. Hu, A. Szab, T. Portenier, P. Favaro, and M. Zwicker. Disentangling factors of variation by mixing them. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.

[9] A. H. Jha, S. Anand, M. Singh, and V. Veeravasarapu. Disentangling factors of variation with cycle-consistent variational autoencoders. In The European Conference on Computer Vision (ECCV), September 2018.

[10] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 86(11):2278–2324, 1998.

[11] H.-Y. Lee, H.-Y. Tseng, J.-B. Huang, M. Singh, and M.-H. Yang. Diverse image-to-image translation via disentangled representations. In The European Conference on Computer Vision (ECCV), September 2018.

[12] A. H. Liu, Y.-C. Liu, Y.-Y. Yeh, and Y.-C. F. Wang. A unified feature disentangler for multi-domain image translation and manipulation. In S. Bengio, H.Wallach, H. Larochelle, K. Grauman, N. Cesa-Bianchi, and R. Garnett, editors, Advances in Neural Information Processing Systems 31, pages 2595–2604. Curran Associates, Inc., 2018.

[13] Y. Liu, F. Wei, J. Shao, L. Sheng, J. Yan, and X. Wang. Exploring disentangled feature representation beyond face identification. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2018.

[14] Z. Liu, P. Luo, X.Wang, and X. Tang. Deep learning face attributes in the wild. In Proceedings of the IEEE International Conference on Computer Vision, pages 3730–3738, 2015.

[15] M. F. Mathieu, J. J. Zhao, J. Zhao, A. Ramesh, P. Sprechmann, and Y. LeCun. Disentangling factors of variation in deep representation using adversarial training. In D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, and R. Garnett, editors, Advances in Neural Information Processing Systems 29, pages 5040–5048. Curran Associates, Inc., 2016.

[16] S. Narayanaswamy, T. B. Paige, J.-W. van de Meent, A. Desmaison, N. Goodman, P. Kohli, F. Wood, and P. Torr. Learning disentangled representations with semi-supervised deep generative models. In I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems 30, pages 5925–5935. Curran Associates, Inc., 2017.

[17] Z. Shu, M. Sahasrabudhe, R. Alp Guler, D. Samaras, N. Paragios, and I. Kokkinos. Deforming autoencoders: Unsupervised disentangling of shape and appearance. In The European Conference on Computer Vision (ECCV), September 2018.

[18] Z. Shu, E. Yumer, S. Hadap, K. Sunkavalli, E. Shechtman, and D. Samaras. Neural face editing with intrinsic image disentangling. In The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 2017.

[19] J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros. Unpaired image-to-image translation using cycle-consistent adversarial networks. arXiv preprint, 2017.