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Few-shot class-incremental learning (FSCIL) has been proposed aiming to enable a deep learning system to incrementally learn new classes with limited data.
French, R.M.: Catastrophic forgetting in connectionist networks. Trends in Cognitive Sciences 3
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Felzenszwalb, P.F., Girshick, R.B., McAllester, D., Ramanan, D.: Object detection with discriminatively trained part-based models. IEEE Transactions on Pattern Analysis and Machine Intelligence 32
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Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images (2009)
2009
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Wah, C., Branson, S., Welinder, P., Perona, P., Belongie, S.: The caltech-ucsd birds-200-2011 dataset (2011)
2011
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LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521
2015
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Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., et al.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115
2015
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He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2016)
2016
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Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. International Conference on Learning Representations (2016)
2016
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Rebuffi, S.A., Kolesnikov, A., Sperl, G., Lampert, C.H.: icarl: Incremental classifier and representation learning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2017)
2017
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Shin, H., Lee, J.K., Kim, J., Kim, J.: Continual learning with deep generative replay. In: Advances in Neural Information Processing Systems (2017)
2017
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Castro, F.M., Marín-Jiménez, M.J., Guil, N., Schmid, C., Alahari, K.: End-to-end incremental learning. In: European Confererence on Computer Vison (2018)
2018
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Chen, H., Wang, Y., Xu, C., Yang, Z., Liu, C., Shi, B., Xu, C., Xu, C., Tian, Q.: Data-free learning of student networks. In: IEEE International Conference on Computer Vision (2019)
2019
Cited alongside, same era.
Hou, S., Pan, X., Loy, C.C., Wang, Z., Lin, D.: Learning a unified classifier incrementally via rebalancing. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2019)
2019
Cited alongside, same era.
Micaelli, P., Storkey, A.J.: Zero-shot knowledge transfer via adversarial belief matching. In: Advances in Neural Information Processing Systems (2019)
2019
Cited alongside, same era.
Caron, M., Misra, I., Mairal, J., Goyal, P., Bojanowski, P., Joulin, A.: Unsupervised learning of visual features by contrasting cluster assignments. Advances in Neural Information Processing Systems 33
2020
Cited alongside, same era.
Cheraghian, A., Rahman, S., Ramasinghe, S., Fang, P., Simon, C., Petersson, L., Harandi, M.: Synthesized feature based few-shot class-incremental learning on a mixture of subspaces. In: IEEE International Conference on Computer Vision (2021)
2021
Later among the works it cites.
Kim, T., Oh, J., Kim, N.Y., Cho, S., Yun, S.Y.: Comparing kullback-leibler divergence and mean squared error loss in knowledge distillation. In: International Joint Conference on Artificial Intelligence (2021)
2021
Later among the works it cites.
Shankarampeta, A.R., Yamauchi, K.: Few-shot class incremental learning with generative feature replay. In: International Conference on Pattern Recognition Applications and Methods. pp. 259–267 (2021)
2021
Later among the works it cites.
Smith, J., Hsu, Y.C., Balloch, J., Shen, Y., Jin, H., Kira, Z.: Always be dreaming: A new approach for data-free class-incremental learning. In: IEEE International Conference on Computer Vision (2021)
2021
Later among the works it cites.
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Chen, K., Chen, Y., Han, C., Sang, N., Gao, C.: Hard sample mining makes person re-identification more efficient and accurate. Neurocomputing 382
2020
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning. pp. 1597–1607. PMLR (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Cong, Y., Zhao, M., Li, J., Wang, S., Carin, L.: Gan memory with no forgetting. Advances in Neural Information Processing Systems (2020)
2020
Cited alongside, same era.
Tao, X., Hong, X., Chang, X., Dong, S., Wei, X., Gong, Y.: Few-shot class-incremental learning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
2020
Cited alongside, same era.
Yin, H., Molchanov, P., Alvarez, J.M., Li, Z., Mallya, A., Hoiem, D., Jha, N.K., Kautz, J.: Dreaming to distill: Data-free knowledge transfer via deepinversion. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2020)
2020
Cited alongside, same era.
Caron, M., Touvron, H., Misra, I., Jégou, H., Mairal, J., Bojanowski, P., Joulin, A.: Emerging properties in self-supervised vision transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 9650–9660 (2021)
2021
Cited alongside, same era.
2021
Later among the works it cites.
Zhang, C., Song, N., Lin, G., Zheng, Y., Pan, P., Xu, Y.: Few-shot incremental learning with continually evolved classifiers. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)
2021
Later among the works it cites.
Zhu, K., Cao, Y., Zhai, W., Cheng, J., Zha, Z.J.: Self-promoted prototype refinement for few-shot class-incremental learning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition (2021)
2021
Later among the works it cites.
Chi, Z., Gu, L., Liu, H., Wang, Y., Yu, Y., Tang, J.: Metafscil: A meta-learning approach for few-shot class incremental learning. In: IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 14166–14175 (2022)
2022
Closest in time.
Fini, E., da Costa, V.G.T., Alameda-Pineda, X., Ricci, E., Alahari, K., Mairal, J.: Self-supervised models are continual learners. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9621–9630 (2022)
2022
Closest in time.
Liang, H., Quader, N., Chi, Z., Chen, L., Dai, P., Lu, J., Wang, Y.: Self-supervised spatiotemporal representation learning by exploiting video continuity. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 36, pp. 1564–1573 (2022)
2022
Closest in time.