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ACKNOWLEDGEMENTS
This work is funded by the European Regional Devel-
opment Fund (ERDF) under the grant number 100-
241-945.
REFERENCES
Cui, Y., Zhou, F., Lin, Y., and Belongie, S. (2016).
Fine-Grained Categorization and Dataset Bootstrap-
ping Using Deep Metric Learning with Humans in the
Loop. In 2016 IEEE Conference on Computer Vision
and Pattern Recognition (CVPR), pages 1153–1162,
Las Vegas, NV, USA. IEEE.
Deng, J., Dong, W., Socher, R., Li, L.-J., Kai Li, and Li
Fei-Fei (2009). ImageNet: A large-scale hierarchical
image database. In 2009 IEEE Conference on Com-
puter Vision and Pattern Recognition, pages 248–255,
Miami, FL.
Dong-Hyun Lee (2013). Pseudo-Label : The Simple and
Efficient Semi-Supervised Learning Method for Deep
Neural Networks. In ICML 2013 Workshop: Chal-
lenges in Representation Learning (WREPL), Atlanta,
Georgia, USA.
Enguehard, J., O’Halloran, P., and Gholipour, A. (2019).
Semi-Supervised Learning With Deep Embedded
Clustering for Image Classification and Segmentation.
IEEE Access, 7:11093–11104.
Gal, Y., Islam, R., and Ghahramani, Z. (2017). Deep
Bayesian Active Learning with Image Data. In
ICML’17 Proceedings of the 34th International Con-
ference on Machine Learning, Sydney, Australia.
Goodfellow, I. J., Mirza, M., Xiao, D., Courville, A.,
and Bengio, Y. (2013). An Empirical Investigation
of Catastrophic Forgetting in Gradient-Based Neu-
ral Networks. arXiv:1312.6211 [cs, stat]. arXiv:
1312.6211.
Han, J., Zhang, D., Cheng, G., Liu, N., and Xu, D. (2018).
Advanced Deep-Learning Techniques for Salient and
Category-Specific Object Detection: A Survey. IEEE
Signal Processing Magazine, 35(1):84–100.
Hayes, T. L., Cahill, N. D., and Kanan, C. (2018). Memory
Efficient Experience Replay for Streaming Learning.
arXiv:1809.05922 [cs, stat]. arXiv: 1809.05922.
He, K., Zhang, X., Ren, S., and Sun, J. (2016). Deep Resid-
ual Learning for Image Recognition. In 2016 IEEE
Conference on Computer Vision and Pattern Recogni-
tion (CVPR), pages 770–778, Las Vegas, NV, USA.
K
¨
ading, C., Rodner, E., Freytag, A., and Denzler, J. (2017).
Fine-Tuning Deep Neural Networks in Continuous
Learning Scenarios. In Chen, C.-S., Lu, J., and Ma,
K.-K., editors, Computer Vision – ACCV 2016 Work-
shops, volume 10118, pages 588–605. Springer Inter-
national Publishing, Cham.
Kemker, R., McClure, M., Abitino, A., Hayes, T., and
Kanan, C. (2018). Measuring Catastrophic Forgetting
in Neural Networks. New Orleans, Louisiana, USA.
Kirkpatrick, J., Pascanu, R., Rabinowitz, N., Veness, J.,
Desjardins, G., Rusu, A. A., Milan, K., Quan, J.,
Ramalho, T., Grabska-Barwinska, A., Hassabis, D.,
Clopath, C., Kumaran, D., and Hadsell, R. (2017).
Overcoming catastrophic forgetting in neural net-
works. Proceedings of the National Academy of Sci-
ences, 114(13):3521–3526.
Krasin, I., Duerig, T., Alldrin, N., Ferrari, V., Abu-
El-Haija, S., Kuznetsova, A., Rom, H., Uijlings,
J., Popov, S., Veit, A., Belongie, S., Gomes, V.,
Gupta, A., Sun, C., Chechik, G., Cai, D., Feng, Z.,
Narayanan, D., and Murphy, K. (2017). Openimages:
A public dataset for large-scale multi-label and multi-
class image classification. Dataset available from
https://github.com/openimages.
Li, Z. and Hoiem, D. (2018). Learning without Forgetting.
IEEE Transactions on Pattern Analysis and Machine
Intelligence, 40(12):2935–2947.
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ra-
manan, D., Doll
´
ar, P., and Zitnick, C. L. (2014). Mi-
crosoft COCO: Common Objects in Context. In Fleet,
D., Pajdla, T., Schiele, B., and Tuytelaars, T., editors,
Computer Vision – ECCV 2014, volume 8693, pages
740–755. Springer International Publishing, Cham.
Lomonaco, V. and Maltoni, D. (2017). CORe50: a
New Dataset and Benchmark for Continuous Object
Recognition. In Proceedings of the 1st Annual Con-
ference on Robot Learning, California, USA.
Maltoni, D. and Lomonaco, V. (2019). Continuous learn-
ing in single-incremental-task scenarios. Neural Net-
works, 116:56–73.
McCloskey, M. and Cohen, N. J. (1989). Catastrophic In-
terference in Connectionist Networks: The Sequen-
tial Learning Problem. In Psychology of Learning and
Motivation, volume 24, pages 109–165. Elsevier.
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and
Wermter, S. (2019). Continual lifelong learning with
neural networks: A review. Neural Networks, 113:54–
71.
Rasmus, A., Valpola, H., Honkala, M., Berglund, M., and
Raiko, T. (2015). Semi-Supervised Learning with
Ladder Networks. In NIPS’15 Proceedings of the 28th
International Conference on Neural Information Pro-
cessing Systems, volume 2, pages 3546–3554, Mon-
treal, Canada. MIT Press.
Rebuffi, S.-A., Kolesnikov, A., Sperl, G., and Lampert,
C. H. (2017). iCaRL: Incremental Classifier and Rep-
resentation Learning. In The IEEE Conference on
Computer Vision and Pattern Recognition (CVPR),
Honolulu, Hawaii.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S.,
Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bern-
stein, M., Berg, A. C., and Fei-Fei, L. (2015). Ima-
geNet Large Scale Visual Recognition Challenge. In-
ternational Journal of Computer Vision, 115(3):211–
252.
Rusu, A. A., Rabinowitz, N. C., Desjardins, G., Soyer, H.,
Kirkpatrick, J., Kavukcuoglu, K., Pascanu, R., and
Hadsell, R. (2016). Progressive Neural Networks.
arXiv:1606.04671 [cs]. arXiv: 1606.04671.
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