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The limits and potentials of deep learning for robotics
Sünderhauf, N., Brock, O., Scheirer, W. J., Hadsell, R., Fox, D., Leitner, J., Upcroft, B., Abbeel, P., Burgard, W., Milford, M., and Corke, P. (2018) · 2018
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Reinforcement Learning: An Introduction
Sutton, R. S. and Barto, A. G. (2018) · 2018
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Wang, T., Zhu, J., Torralba, A., and Efros, A. A. (2018) · 2018
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Large scale GAN training for high fidelity natural image synthesis
Brock, A., Donahue, J., and Simonyan, K. (2019) · 2019
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Large-scale study of curiosity-driven learning
Burda, Y., Edwards, H., Pathak, D., Storkey, A., Darrell, T., and Efros, A. A. (2019) · 2019
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Efficient lifelong learning with a-gem
Chaudhry, A., Ranzato, M., Rohrbach, M., and Elhoseiny, M. (2019) · 2019
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Exploring to learn visual saliency: The rl-iac approach
Craye, C., Lesort, T., Filliat, D., and Goudou, J.-F. (2019) · 2019
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Classifier training from a generative model
Dat, P. T., Dutt, A., Pellerin, D., and Quénot, G. (2019) · 2019
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Learning without memorizing
Dhar, P., Singh, R. V., Peng, K.-C., Wu, Z., and Chellappa, R. (2019) · 2019
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Learning a unified classifier incrementally via rebalancing
Hou, S., Pan, X., Loy, C. C., Wang, Z., and Lin, D. (2019) · 2019
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Meta-learning representations for continual learning
Javed, K. and White, M. (2019) · 2019
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Continual reinforcement learning deployed in real-life using policydistillation and sim2real transfer
Kalifou, R. T., Caselles-Dupré, H., Lesort, T., Sun, T., Diaz-Rodriguez, N., and Filliat, D. (2019) · 2019
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A style-based generator architecture for generative adversarial networks
Karras, T., Laine, S., and Aila, T. (2019) · 2019
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Continual learning: A comparative study on how to defy forgetting in classification tasks
Lange, M. D., Aljundi, R., Masana, M., Parisot, S., Jia, X., Leonardis, A., Slabaugh, G., and Tuytelaars, T. (2019) · 2019
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Training discriminative models to evaluate generative ones
Lesort, T., Stoian, A., Goudou, J.-F., and Filliat, D. (2019e) · 2019
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Continual learning with deep architectures
Lomonaco, V. (2019) · 2019
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Continuous learning in single-incremental-task scenarios
Maltoni, D. and Lomonaco, V. (2019) · 2019
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Knowledge distillation for incremental learning in semantic segmentation
Michieli, U. and Zanuttigh, P. (2019) · 2019
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Dota 2 with Large Scale Deep Reinforcement Learning
OpenAI, :, Berner, C., Brockman, G., Chan, B., Cheung, V., Dębiak, P., Dennison, C., Farhi, D., Fischer, Q., Hashme, S., Hesse, C., Józefowicz, R., Gray, S., Olsson, C., Pachocki, J., Petrov, M., Pondé de Oliveira Pinto, H., Raiman, J., Salimans, T., Schlatter, J., Schneider, J., Sidor, S., Sutskever, I., Tang, J., Wolski, F., and Zhang, S. (2019) · 2019
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Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., and Wermter, S. (2019) · 2019
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Pytorch: An imperative style, high-performance deep learning library
Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., Antiga, L., Desmaison, A., Kopf, A., Yang, E., DeVito, Z., Raison, M., Tejani, A., Chilamkurthy, S., Steiner, B., Fang, L., Bai, J., and Chintala, S. (2019) · 2019
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A comprehensive, application-oriented study of catastrophic forgetting in DNNs
Pfulb, B. and Gepperth, A. (2019) · 2019
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Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
Raffin, A., Hill, A., Traoré, K. R., Lesort, T., Díaz-Rodríguez, N., and Filliat, D. (2019) · 2019
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Classification accuracy score for conditional generative models
Ravuri, S. and Vinyals, O. (2019) · 2019
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Closed-loop memory gan for continual learning
Rios, A. and Itti, L. (2019) · 2019
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Openloris-object: A dataset and benchmark towards lifelong object recognition
She, Q., Feng, F., Hao, X., Yang, Q., Lan, C., Lomonaco, V., Shi, X., Wang, Z., Guo, Y., Zhang, Y., Qiao, F., and Chan, R. H. M. (2019) · 2019
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Are we ready for service robots? the openloris-scene datasets for lifelong slam
Shi, X., Li, D., Zhao, P., Tian, Q., Tian, Y., Long, Q., Zhu, C., Song, J., Qiao, F., Song, L., Guo, Y., Wang, Z., Zhang, Y., Qin, B., Yang, W., Wang, F., Chan, R. H. M., and She, Q. (2019) · 2019
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Exploring Applications of Deep Reinforcement Learning for Real-world Autonomous Driving Systems
Talpaert, V., Sobh, I., Kiran, B., Mannion, P., Yogamani, S., El-Sallab, A., and Pérez, P. (2019) · 2019
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A survey of zero-shot learning: Settings, methods, and applications
Wang, W., Zheng, V. W., Yu, H., and Miao, C. (2019) · 2019
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Large scale incremental learning
Wu, Y., Chen, Y., Wang, L., Ye, Y., Liu, Z., Guo, Y., and Fu, Y. (2019) · 2019
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Scail: Classifier weights scaling for class incremental learning
Belouadah, E. and Popescu, A. (2020) · 2020
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Online fast adaptation and knowledge accumulation: a new approach to continual learning
Caccia, M., Rodriguez, P., Ostapenko, O., Normandin, F., Lin, M., Caccia, L., Laradji, I., Rish, I., Lacoste, A., Vazquez, D., et al. (2020) · 2020
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Continual learning data former
LESORT, T. (2020) · 2020
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Continual learning for robotics: Definition, framework, learning strategies, opportunities and challenges
Lesort, T., Lomonaco, V., Stoian, A., Maltoni, D., Filliat, D., and Díaz-Rodríguez, N. (2020) · 2020
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Multi-task self-supervised visual learning
Doersch, C. and Zisserman, A. (2017) · 2060
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