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Despite their success for object detection, convolutional neural networks are ill-equipped for incremental learning, i.e., adapting the original model trained on a set of classes to additionally detect objects of new classes, in the absence of the initial training data.
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R. M. French, B. Ans, and S. Rousset · 2001
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Learn++: An incremental learning algorithm for supervised neural networks
R. Polikar, L. Upda, S. S. Upda, and V. Honavar · 2001
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ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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NEIL: Extracting visual knowledge from web data
X. Chen, A. Shrivastava, and A. Gupta · 2013
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Distance-based image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Tensorflow: Large-scale machine learning on heterogeneous systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, et al · 2015
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Semantic image segmentation with deep convolutional nets and fully connected CRFs
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Fast R-CNN
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E. Tzeng, J. Hoffman, T. Darrell, and K. Saenko · 2015
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Inside-outside net: Detecting objects in context with skip pooling and recurrent neural networks
S. Bell, C. L. Zitnick, K. Bala, and R. Girshick · 2016
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
I. J. Goodfellow, M. Mirza, D. Xiao, A. Courville, and Y. Bengio · 2014
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Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
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Microsoft COCO: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Learning and transferring mid-level image representations using convolutional neural networks
M. Oquab, L. Bottou, I. Laptev, and J. Sivic · 2014
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Incremental learning of NCM forests for large-scale image classification
M. Ristin, M. Guillaumin, J. Gall, and L. V. Gool · 2014
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How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
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Cross modal distillation for supervision transfer
S. Gupta, J. Hoffman, and J. Malik · 2016
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Learning without forgetting
Z. Li and D. Hoiem · 2016
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Policy distillation
A. A. Rusu, S. G. Colmenarejo, C. Gulcehre, G. Desjardins, J. Kirkpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell · 2016
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A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell · 2016
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, D. Hassabis, C. Clopath, D. Kumaran, and R. Hadsell · 2017
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iCaRL: Incremental classifier and representation learning
S.-A. Rebuffi, A. Kolesnikov, and C. H. Lampert · 2017
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