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This work investigates the entanglement between Continual Learning (CL) and Transfer Learning (TL).
Robinson, A.H., Cherry, C.: Results of a prototype television bandwidth compression scheme. In: Proceedings of the IEEE (1967)
1967
Earlier work this paper cites.
Ziv, J., Lempel, A.: A universal algorithm for sequential data compression. IEEE Transactions on information theory (1977)
1977
Earlier work this paper cites.
Vitter, J.S.: Random sampling with a reservoir. ACM Transactions on Mathematical Software (1985)
1985
Earlier work this paper cites.
McCloskey, M., Cohen, N.J.: Catastrophic interference in connectionist networks: The sequential learning problem. Psychology of learning and motivation (1989)
1989
Earlier work this paper cites.
Ratcliff, R.: Connectionist models of recognition memory: constraints imposed by learning and forgetting functions. Psychological Review (1990)
1990
Earlier work this paper cites.
Robins, A.: Catastrophic forgetting, rehearsal and pseudorehearsal. Connection Science (1995)
1995
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., et al.: Learning multiple layers of features from tiny images. Tech. rep., Citeseer (2009)
2009
Earlier work this paper cites.
Pan, S.J., Yang, Q.: A survey on transfer learning. IEEE Transactions on knowledge and data engineering (2009)
2009
Earlier work this paper cites.
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: Reading digits in natural images with unsupervised feature learning. In: Advances in Neural Information Processing Systems (2011)
2011
Earlier work this paper cites.
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: How transferable are features in deep neural networks? In: Advances in Neural Information Processing Systems (2014)
2014
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In: IEEE International Conference on Computer Vision (2015)
2015
Earlier work this paper cites.
Hinton, G., Vinyals, O., Dean, J.: Distilling the knowledge in a neural network. In: Neural Information Processing Systems Workshops (2015)
2015
Earlier work this paper cites.
Ren, S., He, K., Girshick, R., Sun, J.: Faster r-cnn: Towards real-time object detection with region proposal networks. In: Advances in Neural Information Processing Systems (2015)
2015
Earlier work this paper cites.
Romero, A., Ballas, N., Kahou, S.E., Chassang, A., Gatta, C., Bengio, Y.: Fitnets: Hints for thin deep nets. In: International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
Stanford: Tiny ImageNet Challenge (CS231n) (2015), https://www.kaggle.com/c/tiny-imagenet
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Silver, D., Huang, A., Maddison, C.J., Guez, A., Sifre, L., Van Den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al.: Mastering the game of go with deep neural networks and tree search. Nature (2016)
2016
Earlier work this paper cites.
De Vries, H., Strub, F., Mary, J., Larochelle, H., Pietquin, O., Courville, A.C.: Modulating early visual processing by language. In: Advances in Neural Information Processing Systems (2017)
2017
Earlier work this paper cites.
Finn, C., Abbeel, P., Levine, S.: Model-agnostic meta-learning for fast adaptation of deep networks. In: International Conference on Machine Learning (2017)
2017
Earlier work this paper cites.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: IEEE International Conference on Computer Vision (2017)
2017
Earlier work this paper cites.
Jang, E., Gu, S., Poole, B.: Categorical reparameterization with gumbel-softmax. In: International Conference on Learning Representations (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., et al.: Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences (2017)
2017
Earlier work this paper cites.
Li, Z., Hoiem, D.: Learning without forgetting. IEEE Transactions on Pattern Analysis and Machine Intelligence (2017)
2017
Cited alongside, same era.
Long, M., Zhu, H., Wang, J., Jordan, M.I.: Deep transfer learning with joint adaptation networks. In: International Conference on Machine Learning (2017)
2017
Cited alongside, same era.
Lopez-Paz, D., Ranzato, M.: Gradient episodic memory for continual learning. In: Advances in Neural Information Processing Systems (2017)
2017
Cited alongside, same era.
Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2017)
2017
Cited alongside, same era.
Yim, J., Joo, D., Bae, J., Kim, J.: A gift from knowledge distillation: Fast optimization, network minimization and transfer learning. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2017)
Parisi, G.I., Kemker, R., Part, J.L., Kanan, C., Wermter, S.: Continual lifelong learning with neural networks: A review. Neural Networks (2019)
2019
Later among the works it cites.
Porrello, A., Vincenzi, S., Buzzega, P., Calderara, S., Conte, A., Ippoliti, C., Candeloro, L., Di Lorenzo, A., Dondona, A.C.: Spotting insects from satellites: modeling the presence of culicoides imicola through deep cnns. In: International Conference on Signal-Image Technology & Internet-Based Systems (2019)
2019
Later among the works it cites.
Riemer, M., Cases, I., Ajemian, R., Liu, M., Rish, I., Tu, Y., Tesauro, G.: Learning to Learn without Forgetting by Maximizing Transfer and Minimizing Interference. In: International Conference on Learning Representations (2019)
2019
Later among the works it cites.
Vinyals, O., Babuschkin, I., Czarnecki, W.M., Mathieu, M., Dudzik, A., Chung, J., Choi, D.H., Powell, R., Ewalds, T., Georgiev, P., et al.: Grandmaster level in starcraft ii using multi-agent reinforcement learning. Nature (2019)
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2017
Cited alongside, same era.
Zenke, F., Poole, B., Ganguli, S.: Continual learning through synaptic intelligence. In: International Conference on Machine Learning (2017)
2017
Cited alongside, same era.
Chaudhry, A., Dokania, P.K., Ajanthan, T., Torr, P.H.: Riemannian walk for incremental learning: Understanding forgetting and intransigence. In: Proceedings of the European Conference on Computer Vision (2018)
2018
Cited alongside, same era.
Farquhar, S., Gal, Y.: Towards Robust Evaluations of Continual Learning. In: International Conference on Machine Learning Workshop (2018)
2018
Cited alongside, same era.
Furlanello, T., Lipton, Z.C., Tschannen, M., Itti, L., Anandkumar, A.: Born again neural networks. In: International Conference on Machine Learning (2018)
2018
Cited alongside, same era.
Ilse, M., Tomczak, J., Welling, M.: Attention-based deep multiple instance learning. In: International Conference on Machine Learning (2018)
2018
Cited alongside, same era.
Long, M., Cao, Z., Wang, J., Jordan, M.I.: Conditional adversarial domain adaptation. In: Advances in Neural Information Processing Systems (2018)
2018
Cited alongside, same era.
Mallya, A., Lazebnik, S.: Packnet: Adding multiple tasks to a single network by iterative pruning. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2018)
2018
Cited alongside, same era.
2019
Later among the works it cites.
Wang, K., Gao, X., Zhao, Y., Li, X., Dou, D., Xu, C.Z.: Pay attention to features, transfer learn faster cnns. In: International Conference on Learning Representations (2019)
2019
Later among the works it cites.
Abati, D., Tomczak, J., Blankevoort, T., Calderara, S., Cucchiara, R., Bejnordi, B.E.: Conditional channel gated networks for task-aware continual learning. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2020)
2020
Later among the works it cites.
Aguilar, G., Ling, Y., Zhang, Y., Yao, B., Fan, X., Guo, C.: Knowledge distillation from internal representations. In: Proceedings of the AAAI Conference on Artificial Intelligence (2020)
2020
Later among the works it cites.
Buzzega, P., Boschini, M., Porrello, A., Abati, D., Calderara, S.: Dark Experience for General Continual Learning: a Strong, Simple Baseline. In: Advances in Neural Information Processing Systems (2020)
2020
Later among the works it cites.
Buzzega, P., Boschini, M., Porrello, A., Calderara, S.: Rethinking Experience Replay: a Bag of Tricks for Continual Learning. In: International Conference on Pattern Recognition (2020)
2020
Later among the works it cites.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International Conference on Machine Learning (2020)
2020
Later among the works it cites.
Müller, R., Kornblith, S., Hinton, G.: Subclass distillation. arXiv preprint arXiv:2002.03936 (2020)
2020
Later among the works it cites.
Yu, L., Twardowski, B., Liu, X., Herranz, L., Wang, K., Cheng, Y., Jui, S., Weijer, J.v.d.: Semantic drift compensation for class-incremental learning. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2020)
2020
Later among the works it cites.
Allegretti, S., Bolelli, F., Pollastri, F., Longhitano, S., Pellacani, G., Grana, C.: Supporting Skin Lesion Diagnosis with Content-Based Image Retrieval. In: International Conference on Pattern Recognition (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
Cha, H., Lee, J., Shin, J.: Co2l: Contrastive continual learning. In: IEEE International Conference on Computer Vision (2021)
2021
Later among the works it cites.
De Lange, M., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., Tuytelaars, T.: A continual learning survey: Defying forgetting in classification tasks. IEEE Transactions on Pattern Analysis and Machine Intelligence (2021)
2021
Later among the works it cites.
Mehta, S.V., Patil, D., Chandar, S., Strubell, E.: An empirical investigation of the role of pre-training in lifelong learning. In: International Conference on Machine Learning (2021)
2021
Later among the works it cites.
Smith, J., Balloch, J., Hsu, Y.C., Kira, Z.: Memory-efficient semi-supervised continual learning: The world is its own replay buffer. In: International Joint Conference on Neural Networks (2021)
2021
Later among the works it cites.
Bellitto, G., Pennisi, M., Palazzo, S., Bonicelli, L., Boschini, M., Calderara, S., Spampinato, C.: Effects of auxiliary knowledge on continual learning. In: International Conference on Pattern Recognition (2022)
2022
Closest in time.
2022
Closest in time.
Caccia, L., Aljundi, R., Asadi, N., Tuytelaars, T., Pineau, J., Belilovsky, E.: New Insights on Reducing Abrupt Representation Change in Online Continual Learning. In: International Conference on Learning Representations (2022)
2022
Closest in time.
Monti, A., Porrello, A., Calderara, S., Coscia, P., Ballan, L., Cucchiara, R.: How many observations are enough? knowledge distillation for trajectory forecasting. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition (2022)
2022
Closest in time.
Shaheen, K., Hanif, M.A., Hasan, O., Shafique, M.: Continual learning for real-world autonomous systems: Algorithms, challenges and frameworks. Journal of Intelligent & Robotic Systems (2022)
2022
Closest in time.