Fetching the paper…
Reading the bibliography…
The Contrastive Language-Image Pre-training (CLIP) Model is a recently proposed large-scale pre-train model which attracts increasing attention in the computer vision community.
M. McCloskey and N. J. Cohen, “Catastrophic interference in connectionist networks: The sequential learning problem,” in Psychology of learning and motivation . Elsevier, 1989, vol. 24, pp. 109–165
1989
Earlier work this paper cites.
D. J. MacKay, “A practical bayesian framework for backpropagation networks,” Neural computation , vol. 4, no. 3, pp. 448–472, 1992
1992
Earlier work this paper cites.
S.-A. Rebuffi, A. Kolesnikov, G. Sperl, and C. H. Lampert, “icarl: Incremental classifier and representation learning,” in Proceedings of the IEEE conference on Computer Vision and Pattern Recognition , 2017, pp. 2001–2010
2010
Earlier work this paper cites.
2013
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” Advances in neural information processing systems , vol. 27, 2014
2014
Earlier work this paper cites.
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision , 2014, pp. 740–755
2014
Earlier work this paper cites.
2015
Earlier work this paper cites.
A. Karpathy and L. Fei-Fei, “Deep visual-semantic alignments for generating image descriptions,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 3128–3137
2015
Earlier work this paper cites.
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein et al. , “Imagenet large scale visual recognition challenge,” International journal of computer vision , vol. 115, no. 3, pp. 211–252, 2015
2015
Earlier work this paper cites.
D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , 2015
2015
Earlier work this paper cites.
Z. Li and D. Hoiem, “Learning without forgetting,” IEEE transactions on pattern analysis and machine intelligence , vol. 40, no. 12, pp. 2935–2947, 2017
2017
Earlier work this paper cites.
S. Lee, J. Kim, J. Jun, J. Ha, and B. Zhang, “Overcoming catastrophic forgetting by incremental moment matching,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , 2017, pp. 4652–4662
2017
Earlier work this paper cites.
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska et al. , “Overcoming catastrophic forgetting in neural networks,” Proceedings of the national academy of sciences , vol. 114, no. 13, pp. 3521–3526, 2017
2017
Earlier work this paper cites.
F. Zenke, B. Poole, and S. Ganguli, “Continual learning through synaptic intelligence,” in International Conference on Machine Learning , 2017, pp. 3987–3995
2017
Earlier work this paper cites.
R. R. Selvaraju, M. Cogswell, A. Das, R. Vedantam, D. Parikh, and D. Batra, “Grad-cam: Visual explanations from deep networks via gradient-based localization,” in Proceedings of the IEEE international conference on computer vision , 2017, pp. 618–626
2017
Earlier work this paper cites.
H. Shin, J. K. Lee, J. Kim, and J. Kim, “Continual learning with deep generative replay,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , 2017, pp. 2990–2999
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, December 4-9, 2017, Long Beach, CA, USA , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
D. Lopez-Paz and M. Ranzato, “Gradient episodic memory for continual learning,” Advances in neural information processing systems , vol. 30, pp. 6467–6476, 2017
2017
Earlier work this paper cites.
B. A. Plummer, L. Wang, C. M. Cervantes, J. C. Caicedo, J. Hockenmaier, and S. Lazebnik, “Flickr30k entities: Collecting region-to-phrase correspondences for richer image-to-sentence models,” International Journal of Computer Vision , vol. 123, no. 1, pp. 74–93, 2017
2017
Cited alongside, same era.
2018
Cited alongside, same era.
J. Schwarz, W. Czarnecki, J. Luketina, A. Grabska-Barwinska, Y. W. Teh, R. Pascanu, and R. Hadsell, “Progress & compress: A scalable framework for continual learning,” in International Conference on Machine Learning , 2018, pp. 4528–4537
2018
Cited alongside, same era.
R. Aljundi, F. Babiloni, M. Elhoseiny, M. Rohrbach, and T. Tuytelaars, “Memory aware synapses: Learning what (not) to forget,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 139–154
A. Douillard, M. Cord, C. Ollion, T. Robert, and E. Valle, “Podnet: Pooled outputs distillation for small-tasks incremental learning,” in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XX 16 , 2020, pp. 86–102
2020
Later among the works it cites.
Y. Liu, Y. Su, A.-A. Liu, B. Schiele, and Q. Sun, “Mnemonics training: Multi-class incremental learning without forgetting,” in Proceedings of the IEEE/CVF conference on Computer Vision and Pattern Recognition , 2020, pp. 12 245–12 254
2020
Later among the works it cites.
A. Prabhu, P. H. Torr, and P. K. Dokania, “Gdumb: A simple approach that questions our progress in continual learning,” in European conference on computer vision , 2020, pp. 524–540
2020
Later among the works it cites.
G.-M. Park, S.-M. Yoo, and J.-H. Kim, “Convolutional neural network with developmental memory for continual learning,” IEEE Transactions on Neural Networks and Learning Systems , vol. 32, no. 6, pp. 2691–2705, 2020
2020
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
D. Isele and A. Cosgun, “Selective experience replay for lifelong learning,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 32, no. 1, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
J. Yoon, E. Yang, J. Lee, and S. J. Hwang, “Lifelong learning with dynamically expandable networks,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings , 2018
2018
Cited alongside, same era.
J. Serra, D. Suris, M. Miron, and A. Karatzoglou, “Overcoming catastrophic forgetting with hard attention to the task,” in International Conference on Machine Learning , 2018, pp. 4548–4557
2018
Cited alongside, same era.
M. Ren, E. Triantafillou, S. Ravi, J. Snell, K. Swersky, J. B. Tenenbaum, H. Larochelle, and R. S. Zemel, “Meta-learning for semi-supervised few-shot classification,” in 6th International Conference on Learning Representations, ICLR 2018, Vancouver, BC, Canada, April 30 - May 3, 2018, Conference Track Proceedings , 2018
2018
Cited alongside, same era.
2019
Cited alongside, same era.
2019
Cited alongside, same era.
C. Sun, A. Myers, C. Vondrick, K. Murphy, and C. Schmid, “Videobert: A joint model for video and language representation learning,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 7464–7473
2019
Cited alongside, same era.
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” in Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event , vol. 139, 2021, pp. 8748–8763
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
C. Simon, P. Koniusz, and M. Harandi, “On learning the geodesic path for incremental learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 1591–1600
2021
Later among the works it cites.
P. Singh, P. Mazumder, P. Rai, and V. P. Namboodiri, “Rectification-based knowledge retention for continual learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 15 282–15 291
2021
Later among the works it cites.
C. Jia, Y. Yang, Y. Xia, Y. Chen, Z. Parekh, H. Pham, Q. V. Le, Y. Sung, Z. Li, and T. Duerig, “Scaling up visual and vision-language representation learning with noisy text supervision,” in Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event , vol. 139, 2021, pp. 4904–4916
2021
Later among the works it cites.
2021
Later among the works it cites.
D.-W. Zhou, Y. Yang, and D.-C. Zhan, “Learning to classify with incremental new class,” IEEE Transactions on Neural Networks and Learning Systems , 2021
2021
Later among the works it cites.
L. Wang, B. Lei, Q. Li, H. Su, J. Zhu, and Y. Zhong, “Triple-memory networks: A brain-inspired method for continual learning,” IEEE Transactions on Neural Networks and Learning Systems , vol. 33, no. 5, pp. 1925–1934, 2021
2021
Later among the works it cites.
S. Yan, J. Xie, and X. He, “Der: Dynamically expandable representation for class incremental learning,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2021, pp. 3014–3023
2021
Later among the works it cites.