Fetching the paper…
Reading the bibliography…
Due to their inference, data representation and reconstruction properties, Variational Autoencoders (VAE) have been successfully used in continual learning classification tasks.
Kantorovitch, L.: On the translocation of masses. Management science 5
1958
Earlier work this paper cites.
Vitter, J.S.: Random sampling with a reservoir. ACM Transactions on Mathematical Software (TOMS) 11
1985
Earlier work this paper cites.
LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. of the IEEE 86
1998
Earlier work this paper cites.
Goldberger, J., Gordon, S., Greenspan, H., et al.: An efficient image similarity measure based on approximations of kl-divergence between two gaussian mixtures. In: Proc. IEEE Int. Conf. on Computer Vision (ICCV). vol. 3, pp. 487–493 (2003)
2003
Earlier work this paper cites.
Krizhevsky, A., Hinton, G.: Learning multiple layers of features from tiny images. Tech. rep. (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Inf. Proc. Systems (NIPS). pp. 1097–1105 (2012)
2012
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. arXiv preprint arXiv:1312.6114 (2013)
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Proc. Advances in Neural Inf. Proc. Systems (NIPS). pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
Lake, B.M., Salakhutdinov, R., Tenenbaum, J.B.: Human-level concept learning through probabilistic program induction. Science 350
2015
Earlier work this paper cites.
Le, Y., Yang, X.: Tiny imagenet visual recognition challenge. CS 231N 7
2015
Earlier work this paper cites.
Courty, N., Flamary, R., Tuia, D., Rakotomamonjy, A.: Optimal transport for domain adaptation. IEEE Trans. on Pattern Analysis and Machine Intelligence 39
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training GANs. In: Proc. Advances in Neural Inf. Proc. Systems (NIPS). pp. 2234–2242 (2016)
2016
Earlier work this paper cites.
Abbasnejad, E., Dick, M., van der Hengel, A.: Infinite variational autoencoder for semi-supervised learning. In: Proc. of IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 5888–5897 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local Nash equilibrium. In: Proc. Advances in Neural Information Processing Systems (NIPS). pp. 6626–6637 (2017)
2017
Earlier work this paper cites.
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., Lerchner, A.: β \beta -VAE: Learning basic visual concepts with a constrained variational framework. In: Proc. Int. Conf. on Learning Representations (ICLR) (2017)
2017
Earlier work this paper cites.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J., Desjardins, G., Rusu, A.A., Milan, K., Quan, J., Ramalho, T., Grabska-Barwinska, A., Hassabis, D., Clopath, C., Kumaran, D., Hadsell, R.: Overcoming catastrophic forgetting in neural networks. Proc. of the National Academy of Sciences (PNAS) 114
2017
Earlier work this paper cites.
Li, Z., Hoiem, D.: Learning without forgetting. IEEE Trans. on Pattern Analysis and Machine Intelligence 40
2017
Earlier work this paper cites.
Liu, M.Y., Breuel, T., Kautz, J.: Unsupervised image-to-image translation networks. In: Advances in Neural Information Processing Systems. pp. 700–708 (2017)
2017
Earlier work this paper cites.
Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. In: Advances in Neural Information Processing Systems. pp. 6467–6476 (2017)
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: iCaRL: Incremental classifier and representation learning. In: Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 2001–2010 (2017)
2017
Cited alongside, same era.
Ren, B., Wang, H., Li, J., Gao, H.: Life-long learning based on dynamic combination model. Applied Soft Computing 56
2017
Cited alongside, same era.
Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. In: Advances in Neural Inf. Proc. Systems (NIPS). pp. 2990–2999 (2017)
2017
Cited alongside, same era.
Achille, A., Eccles, T., Matthey, L., Burgess, C., Watters, N., Lerchner, A., Higgins, I.: Life-long disentangled representation learning with cross-domain latent homologies. In: Proc. Advances in Neural Inf. Proc. Systems (NeurIPS). pp. 9873–9883 (2018)
2018
Cited alongside, same era.
Knoblauch, J., Husain, H., Diethe, T.: Optimal continual learning has perfect memory and is NP-hard. In: Proc. International Conference on Machine Learning (ICML), vol PMLR 119. pp. 5327–5337 (2020)
2020
Later among the works it cites.
2020
Later among the works it cites.
Ye, F., Bors, A.G.: Learning latent representations across multiple data domains using lifelong VAEGAN. In: Proc. European Conf. on Computer Vision (ECCV), vol. LNCS 12365. pp. 777–795 (2020)
2020
Later among the works it cites.
Ye, F., Bors, A.G.: Lifelong learning of interpretable image representations. In: Proc. Int. Conf. on Image Processing Theory, Tools and Applications (IPTA). pp. 1–6 (2020)
2020
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Belghazi, M.I., Baratin, A., Rajeshwar, S., Ozair, S., Bengio, Y., Courville, A., Hjelm, D.: Mutual information neural estimation. In: Proc. Inter. Conference on Machine Learning (ICML), vol. PMLR 80. pp. 531–540 (2018)
2018
Cited alongside, same era.
Chen, L., Dai, S., Pu, Y., Li, C., Su, Q., Carin, L.: Symmetric variational autoencoder and connections to adversarial learning. In: Proc. Int. Conf. on Artificial Intel. and Statistics (AISTATS) 2018, vol. PMLR 84. pp. 661–669 (2018)
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2018
Cited alongside, same era.
Aljundi, R., Lin, M., Goujaud, B., Bengio, Y.: Gradient based sample selection for online continual learning. In: Advances Neural Information Processing Systems (NeurIPS). vol. 33, pp. 11817–11826 (2019)
2019
Cited alongside, same era.
Aljundi, R., Belilovsky, E., Tuytelaars, T., Charlin, L., Caccia, M., Lin, M., Page-Caccia, L.: Online continual learning with maximal interfered retrieval. In: Advances in Neural Information Processing Systems (NeurIPS). vol. 33, pp. 11872–11883 (2019)
2019
Cited alongside, same era.
Aljundi, R., Kelchtermans, K., Tuytelaars, T.: Task-free continual learning. In: Proc. of IEEE/CVF Conf. on Computer Vision and Pattern Recognition. pp. 11254–11263 (2019)
2019
Cited alongside, same era.
2019
Cited alongside, same era.
Ye, F., Bors, A.G.: Mixtures of variational autoencoders. In: 2020 Tenth International Conference on Image Processing Theory, Tools and Applications (IPTA). pp. 1–6 (2020)
2020
Later among the works it cites.
Bang, J., Kim, H., Yoo, Y., Ha, J.W., Choi, J.: Rainbow memory: Continual learning with a memory of diverse samples. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 8218–8227 (2021)
2021
Later among the works it cites.
De Lange, M., Tuytelaars, T.: Continual prototype evolution: Learning online from non-stationary data streams. In: Proc. of the IEEE/CVF Int. Conference on Computer Vision (ICCV). pp. 8250–8259 (2021)
2021
Later among the works it cites.
Egorov, E., Kuzina, A., Burnaev, E.: BooVAE: Boosting approach for continual learning of VAE. Advances in Neural Information Processing Systems (NeurIPS) 35
2021
Later among the works it cites.
Fang, P., Harandi, M., Petersson, L.: Kernel methods in hyperbolic spaces. In: Proc. of the IEEE/CVF Int. Conference on Computer Vision (ICCV). pp. 10665–10674 (2021)
2021
Later among the works it cites.
Fatras, K., Séjourné, T., Flamary, R., Courty, N.: Unbalanced minibatch optimal transport; applications to domain adaptation. In: Int. Conf. on Machine Learning (ICML), vol. PMLR 139. pp. 3186–3197 (2021)
2021
Later among the works it cites.
Lee, S., Goldt, S., Saxe, A.: Continual learning in the teacher-student setup: Impact of task similarity. In: International Conference on Machine Learning (ICML), vol. PMLR 139. pp. 6109–6119 (2021)
2021
Later among the works it cites.
Raghavan, K., Balaprakash, P.: Formalizing the generalization-forgetting trade-off in continual learning. Advances in Neural Information Processing Systems 34
2021
Later among the works it cites.
Tang, S., Chen, D., Zhu, J., Yu, S., Ouyang, W.: Layerwise optimization by gradient decomposition for continual learning. In: Proc. of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 9634–9643 (2021)
2021
Later among the works it cites.
Wang, S., Li, X., Sun, J., Xu, Z.: Training networks in null space of feature covariance for continual learning. In: Proc. of the IEEE/CVF Conf. on Computer Vision and Pattern Recognition (CVPR). pp. 184–193 (2021)
2021
Later among the works it cites.
Ye, F., Bors, A.: Lifelong teacher-student network learning. IEEE Trans. on Pattern Analysis and Machine Intelligence (2021). https://doi.org/10.1109/TPAMI.2021.3092677
2021
Later among the works it cites.
Ye, F., Bors, A.G.: Deep mixture generative autoencoders. IEEE Transactions on Neural Networks and Learning Systems pp. 1–15 (2021). https://doi.org/10.1109/TNNLS.2021.3071401
2021
Later among the works it cites.
Ye, F., Bors, A.G.: Infovaegan: Learning joint interpretable representations by information maximization and maximum likelihood. In: Proc. IEEE Int. Conf. on Image Processing (ICIP). pp. 749–753 (2021). https://doi.org/10.1109/ICIP42928.2021.9506169
2021
Later among the works it cites.
Ye, F., Bors, A.G.: Learning joint latent representations based on information maximization. Information Sciences 567
2021
Later among the works it cites.
Ye, F., Bors, A.G.: Lifelong infinite mixture model based on knowledge-driven Dirichlet process. In: Proc. of the IEEE Int. Conf. on Computer Vision (ICCV) (2021)
2021
Later among the works it cites.
Ye, F., Bors, A.G.: Lifelong mixture of variational autoencoders. IEEE Transactions on Neural Networks and Learning Systems pp. 1–14 (2021). https://doi.org/10.1109/TNNLS.2021.3096457
2021
Later among the works it cites.
Ye, F., Bors, A.G.: Lifelong twin generative adversarial networks. In: Proc. IEEE Int. Conf. on Image Processing (ICIP). pp. 1289–1293 (2021)
2021
Later among the works it cites.
Ye, F., Bors, A.G.: Learning an evolved mixture model for task-free continual learning (2022)
2022
Closest in time.
Ye, F., Bors, A.G.: Lifelong generative modelling using dynamic expansion graph model. In: AAAI on Artificial Intelligence. AAAI Press (2022)
2022
Closest in time.