Towards lifelong object recognition: A dataset and benchmark
Tài liệu tham khảo
Li, 2020, A baseline regularization scheme for transfer learning with convolutional neural networks, Pattern Recognit., 98, 107049, 10.1016/j.patcog.2019.107049
Lopez-Paz, 2017, Gradient episodic memory for continual learning, 6467
Mohassel, 2017, SecureML: a system for scalable privacy-preserving machine learning, 19
Zhou, 2020, Two-stage knowledge transfer framework for image classification, Pattern Recognit., 107, 107529, 10.1016/j.patcog.2020.107529
Khamassi, 2018, Discussion and review on evolving data streams and concept drift adapting, Evolving Syst., 9, 1, 10.1007/s12530-016-9168-2
Kirkpatrick, 2017, Overcoming catastrophic forgetting in neural networks, Proc. Natl. Acad. Sci.(PNAS), 3521, 10.1073/pnas.1611835114
Schwarz, 2018, Progress & compress: a scalable framework for continual learning, 4535
Sünderhauf, 2018, The limits and potentials of deep learning for robotics, Int. J. Rob. Res., 37, 405, 10.1177/0278364918770733
Maltoni, 2016, Semi-supervised tuning from temporal coherence, 2509
She, 2020, OpenLORIS-object: a robotic vision dataset and benchmark for lifelong deep learning, 4767
G.M. Van de Ven, A.S. Tolias, Three scenarios for continual learning, arXiv preprint arXiv:1904.07734(2019).
Mermillod, 2013, The stability-plasticity dilemma: Investigating the continuum from catastrophic forgetting to age-limited learning effects, Front. Psychol., 4, 504, 10.3389/fpsyg.2013.00504
Li, 2017, Learning without forgetting, IEEE Trans. Pattern Anal. Mach.Intell., 40, 2935, 10.1109/TPAMI.2017.2773081
Zenke, 2017, Continual learning through synaptic intelligence, vol. 70, 3987
Masse, 2018, Alleviating catastrophic forgetting using context-dependent gating and synaptic stabilization, Proc. Natl. Acad. Sci. (PNAS), 115, 467, 10.1073/pnas.1803839115
Yoon, 2018, Lifelong learning with dynamically expandable network
Rebuffi, 2017, iCaRL: incremental classifier and representation learning, 2001
Shin, 2017, Continual learning with deep generative replay, 2990
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, Z. Zhang, Y. Fu, Incremental classifier learning with generative adversarial networks, arXiv preprint arXiv:1802.00853(2018).
G.M. van de Ven, A.S. Tolias, Generative replay with feedback connections as a general strategy for continual learning, arXiv preprint arXiv:1809.10635(2018).
Lao, 2021, FoCL: Feature-oriented continual learning for generative models, Pattern Recognit., 120, 108127, 10.1016/j.patcog.2021.108127
Maltoni, 2019, Continuous learning in single-incremental-task scenarios, Neural Netw., 116, 56, 10.1016/j.neunet.2019.03.010
Pellegrini, 2020, Latent replay for real-time continual learning, 10203
LeCun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, P. Perona, Caltech-UCSD birds 200 (2010).
Lai, 2011, A large-scale hierarchical multi-view RGB-D object dataset, 1817
Loghmani, 2018, Recognizing objects in-the-wild: where do we stand?, 2170
Lomonaco, 2017, CORe50: a new dataset and benchmark for continuous object recognition, 17
Wang, 2019, Fast online object tracking and segmentation: a unifying approach
Parisi, 2019, Continual lifelong learning with neural networks: a review, Neural Netw., 10.1016/j.neunet.2019.01.012
He, 2021, Towards non-IID image classification: a dataset and baselines, Pattern Recognit., 110, 107383, 10.1016/j.patcog.2020.107383
Díaz-Rodríguez, 2018, Don’t forget, there is more than forgetting: new metrics for continual learning
Khan, 2021, Transformers in vision: a survey, ACM Comput. Surv. (CSUR)
Kim, 2021, ViLT: vision-and-language transformer without convolution or region supervision, 5583
S.A. Nene, S.K. Nayar, H. Murase, et al., Columbia object image library (coil-20)(1996).
LeCun, 2004, Learning methods for generic object recognition with invariance to pose and lighting, 97
Nilsback, 2008, Automated flower classification over a large number of classes, 722
Krizhevsky, 2009, Learning Multiple Layers of Features from Tiny Images
Wah, 2011, The Caltech-UCSD Birds-200-2011 Dataset