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Continual learning (CL) is a particular machine learning paradigm where the data distribution and learning objective changes through time, or where all the training data and objective criteria are never available at once.
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Why there are complementary learning systems in the hippocampus and neocortex: insights from the successes and failures of connectionist models of learning and memory
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Catastrophic forgetting, rehearsal and pseudorehearsal
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Lifelong robot learning
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A bayesian on-line change detection algorithm with process monitoring applications
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Introduction to reinforcement learning
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Catastrophic forgetting in connectionist networks
R. M. French · 1999
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The scientist in the crib: Minds, brains and how children learn
A. Gopnik, A. Meltzoff, and P. Kuhl · 2001
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Autonomous mental development by robots and animals
J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, and E. Thelen · 2001
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Developmental robotics: a survey
M. Lungarella, G. Metta, R. Pfeifer, and G. Sandini · 2003
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Toward a formal framework for continual learning
M. B. Ring · 2005
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The development of embodied cognition: Six lessons from babies
L. Smith and M. Gasser · 2005
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One-shot learning of object categories
L. Fei-Fei, R. Fergus, and P. Perona · 2006
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Intrinsic motivation systems for autonomous mental development
P.-Y. Oudeyer, F. Kaplan, and V. Hafner · 2007
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Metalearning: Applications to Data Mining
P. Brazdil, C. Giraud-Carrier, C. Soares, and R. Vilalta · 2008
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Using promoters and functional introns in genetic algorithms for neuroevolutionary learning in non-stationary problems
F. Bellas, J. A. Becerra, and R. J. Duro · 2009
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Curriculum learning
Y. Bengio, J. Louradour, R. Collobert, and J. Weston · 2009
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Science of memory: Concepts. henry l. roediger iii, yadin dudai, and susan m. fitzpatrick (eds.). oxford university press, new york, 2007. no. of pages 464. isbn 978-0-19-531044-3.(paperback)
J.-F. Delvenne · 2009
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Learning multiple layers of features from tiny images
A. Krizhevsky, G. Hinton, et al · 2009
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Artificial Intelligence: A Modern Approach
S. Russell and P. Norvig · 2009
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Active learning literature survey
B. Settles · 2009
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Active learning literature survey
B. Settles · 2009
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Computing machinery and intelligence
A. M. Turing · 2009
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Multilevel darwinist brain (mdb): Artificial evolution in a cognitive architecture for real robots
F. Bellas, R. J. Duro, A. Faina, and D. Souto · 2010
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A cognitive developmental robotics architecture for lifelong learning by evolution in real robots
F. Bellas, A. Faiña, G. Varela, and R. J. Duro · 2010
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Toward an architecture for never-ending language learning
A. Carlson, J. Betteridge, B. Kisiel, B. Settles, E. R. Hruschka Jr, and T. M. Mitchell · 2010
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MNIST handwritten digit database
Y. LeCun and C. Cortes · 2010
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Formal theory of creativity, fun, and intrinsic motivation (1990–2010)
J. Schmidhuber · 2010
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Caltech-UCSD Birds 200
P. Welinder, S. Branson, T. Mita, C. Wah, F. Schroff, S. Belongie, and P. Perona · 2010
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Statistics for High-Dimensional Data: Methods, Theory and Applications
P. Bühlmann and S. van de Geer · 2011
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One shot learning of simple visual concepts
B. Lake, R. Salakhutdinov, J. Gross, and J. Tenenbaum · 2011
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Reading digits in natural images with unsupervised feature learning
Y. Netzer, T. Wang, A. Coates, A. Bissacco, B. Wu, and A. Y. Ng · 2011
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Planning to be surprised: Optimal bayesian exploration in dynamic environments
Y. Sun, F. Gomez, and J. Schmidhuber · 2011
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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Learn to swing up and balance a real pole based on raw visual input data
J. Mattner, S. Lange, and M. A. Riedmiller · 2012
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Online incremental feature learning with denoising autoencoders
G. Zhou, K. Sohn, and H. Lee · 2012
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The arcade learning environment: An evaluation platform for general agents
M. G. Bellemare, Y. Naddaf, J. Veness, and M. Bowling · 2013
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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 · 2013
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The stability-plasticity dilemma: investigating the continuum from catastrophic forgetting to age-limited learning effects
M. Mermillod, A. Bugaiska, and P. Bonin · 2013
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Simultaneous on-line discovery and improvement of robotic skill options
F. Stulp, L. Herlant, A. Hoarau, and G. Raiola · 2014
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Herbdisc: Towards lifelong robotic object discovery
A. Collet, B. Xiong, C. Gurau, M. Hebert, and S. S. Srinivasa · 2015
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Exploration strategies for incremental learning of object-based visual saliency
C. Craye, D. Filliat, and J. Goudou · 2015
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The pascal visual object classes challenge: A retrospective
M. Everingham, S. M. A. Eslami, L. Van Gool, C. K. I. Williams, J. Winn, and A. Zisserman · 2015
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Deep spatial autoencoders for visuomotor learning
C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine, and P. Abbeel · 2015
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Distilling the Knowledge in a Neural Network
G. Hinton, O. Vinyals, and J. Dean · 2015
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Learning state representations with robotic priors
R. Jonschkowski and O. Brock · 2015
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Human-level concept learning through probabilistic program induction
B. M. Lake, R. Salakhutdinov, and J. B. Tenenbaum · 2015
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From passive to interactive object learning and recognition through self-identification on a humanoid robot
N. Lyubova, S. Ivaldi, and D. Filliat · 2015
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Never-ending learning
T. Mitchell, W. Cohen, E. Hruscha, P. Talukdar, J. Betteridge, A. Carlson, B. Dalvi, M. Gardner, B. Kisiel, J. Krishnamurthy, N. Lao, K. Mazaitis, T. Mohammad, N. Nakashole, E. Platanios, A. Ritter, M. Samadi, B. Settles, R. Wang, D. Wijaya, A. Gupta, X. Chen, A. Saparov, M. Greaves, and J. Welling · 2015
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Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, S. Petersen, C. Beattie, A. Sadik, I. Antonoglou, H. King, D. Kumaran, D. Wierstra, S. Legg, and D. Hassabis · 2015
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Teaching icub to recognize objects using deep convolutional neural networks
G. Pasquale, C. Ciliberto, F. Odone, L. Rosasco, and L. Natale · 2015
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Visual domain adaptation: A survey of recent advances
V. M. Patel, R. Gopalan, R. Li, and R. Chellappa · 2015
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Lifelong learning with non-iid tasks
A. Pentina and C. H. Lampert · 2015
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2015
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A. A. Rusu, S. Gomez Colmenarejo, C. Gulcehre, G. Desjardins, J. Kirkpatrick, R. Pascanu, V. Mnih, K. Kavukcuoglu, and R. Hadsell · 2015
Cited alongside, same era.
T. Schaul, J. Quan, I. Antonoglou, and D. Silver · 2015
Cited alongside, same era.
Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
F. Yu, Y. Zhang, S. Song, A. Seff, and J. Xiao · 2015
Cited alongside, same era.
Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Cited alongside, same era.
Riemannian walk for incremental learning: Understanding forgetting and intransigence
A. Chaudhry, P. K. Dokania, T. Ajanthan, and P. H. Torr · 2018
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Lifelong machine learning
Z. Chen and B. Liu · 2018
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GEP-PG: Decoupling exploration and exploitation in deep reinforcement learning algorithms
C. Colas, O. Sigaud, and P.-Y. Oudeyer · 2018
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Don’t forget, there is more than forgetting: new metrics for Continual Learning
N. Díaz-Rodríguez, V. Lomonaco, D. Filliat, and D. Maltoni · 2018
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Open-ended learning: a conceptual framework based on representational redescription
S. Doncieux, D. Filliat, N. Díaz-Rodríguez, T. Hospedales, R. Duro, A. Coninx, D. M. Roijers, B. Girard, N. Perrin, and O. Sigaud · 2018
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Surreal: Open-source reinforcement learning framework and robot manipulation benchmark
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M. Bojarski, D. D. Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, X. Zhang, J. Zhao, and K. Zieba · 2016
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Incremental semiparametric inverse dynamics learning
R. Camoriano, S. Traversaro, L. Rosasco, G. Metta, and F. Nori · 2016
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Active Long Term Memory Networks
T. Furlanello, J. Zhao, A. M. Saxe, L. Itti, and B. S. Tjan · 2016
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Incremental learning algorithms and applications
A. Gepperth and B. Hammer · 2016
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A Bio-Inspired Incremental Learning Architecture for Applied Perceptual Problems
A. Gepperth and C. Karaoguz · 2016
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Less-forgetting learning in deep neural networks
H. Jung, J. Ju, M. Jung, and J. Kim · 2016
Cited alongside, same era.
Fine-tuning deep neural networks in continuous learning scenarios
C. Käding, E. Rodner, A. Freytag, and J. Denzler · 2016
Cited alongside, same era.
End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2016
Cited alongside, same era.
L. Fan, Y. Zhu, J. Zhu, Z. Liu, O. Zeng, A. Gupta, J. Creus-Costa, S. Savarese, and L. Fei-Fei · 2018
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Towards robust evaluations of continual learning
S. Farquhar and Y. Gal · 2018
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Memory efficient experience replay for streaming learning
T. L. Hayes, N. D. Cahill, and C. Kanan · 2018
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New metrics and experimental paradigms for continual learning
T. L. Hayes, R. Kemker, N. D. Cahill, and C. Kanan · 2018
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Overcoming catastrophic interference using conceptor-aided backpropagation
X. He and H. Jaeger · 2018
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Quantitatively evaluating GANs with divergences proposed for training
D. J. Im, A. H. Ma, G. W. Taylor, and K. Branson · 2018
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End-to-end race driving with deep reinforcement learning
M. Jaritz, R. de Charette, M. Toromanoff, E. Perot, and F. Nashashibi · 2018
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Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine · 2018
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Fearnet: Brain-inspired model for incremental learning
R. Kemker and C. Kanan · 2018
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Curiosity driven exploration of learned disentangled goal spaces
A. Laversanne-Finot, A. Pere, and P.-Y. Oudeyer · 2018
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State representation learning for control: An overview
T. Lesort, N. Díaz-Rodríguez, J.-F. Goudou, and D. Filliat · 2018
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
A. Mallya, D. Davis, and S. Lazebnik · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
A. Mallya and S. Lazebnik · 2018
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Roboturk: A crowdsourcing platform for robotic skill learning through imitation
A. Mandlekar, Y. Zhu, A. Garg, J. Booher, M. Spero, A. Tung, J. Gao, J. Emmons, A. Gupta, E. Orbay, S. Savarese, and L. Fei-Fei · 2018
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Unicorn: Continual learning with a universal, off-policy agent
D. J. Mankowitz, A. Žídek, A. Barreto, D. Horgan, M. Hessel, J. Quan, J. Oh, H. van Hasselt, D. Silver, and T. Schaul · 2018
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Variational continual learning
C. V. Nguyen, Y. Li, T. D. Bui, and R. E. Turner · 2018
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Computational theories of curiosity-driven learning
P. Oudeyer · 2018
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Lifelong learning of spatiotemporal representations with dual-memory recurrent self-organization
G. I. Parisi, J. Tani, C. Weber, and S. Wermter · 2018
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Deep imitative models for flexible inference, planning, and control
N. Rhinehart, R. McAllister, and S. Levine · 2018
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Online structured laplace approximations for overcoming catastrophic forgetting
H. Ritter, A. Botev, and D. Barber · 2018
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Progress & compress: A scalable framework for continual learning
J. Schwarz, W. Czarnecki, J. Luketina, A. Grabska-Barwinska, Y. W. Teh, R. Pascanu, and R. Hadsell · 2018
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Overcoming catastrophic forgetting with hard attention to the task
J. Serra, D. Suris, M. Miron, and A. Karatzoglou · 2018
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On training recurrent neural networks for lifelong learning
S. Sodhani, S. Chandar, and Y. Bengio · 2018
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Memory-based parameter adaptation
P. Sprechmann, S. Jayakumar, J. Rae, A. Pritzel, A. P. Badia, B. Uria, O. Vinyals, D. Hassabis, R. Pascanu, and C. Blundell · 2018
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The limits and potentials of deep learning for robotics
N. Sünderhauf, O. Brock, W. Scheirer, R. Hadsell, D. Fox, J. Leitner, B. Upcroft, P. Abbeel, W. Burgard, M. Milford, and P. Corke · 2018
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Memory replay gans: Learning to generate new categories without forgetting
C. Wu, L. Herranz, X. Liu, y. wang, J. van de Weijer, and B. Raducanu · 2018
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Incremental classifier learning with generative adversarial networks
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, Z. Zhang, and Y. Fu · 2018
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Lifelong learning with dynamically expandable networks
J. Yoon, E. Yang, J. Lee, and S. J. Hwang · 2018
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Large-scale study of curiosity-driven learning
Y. Burda, H. Edwards, D. Pathak, A. Storkey, T. Darrell, and A. A. Efros · 2019
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S-TRIGGER: Continual State Representation Learning via Self-Triggered Generative Replay
H. Caselles-Dupré, M. Garcia-Ortiz, and D. Filliat · 2019
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Efficient lifelong learning with A-GEM
A. Chaudhry, M. Ranzato, M. Rohrbach, and M. Elhoseiny · 2019
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CURIOUS: Intrinsically motivated modular multi-goal reinforcement learning
C. Colas, P. Fournier, M. Chetouani, O. Sigaud, and P.-Y. Oudeyer · 2019
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Exploring to learn visual saliency: The rl-iac approach
C. Craye, T. Lesort, D. Filliat, and J.-F. Goudou · 2019
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Learning without memorizing
P. Dhar, R. V. Singh, K.-C. Peng, Z. Wu, and R. Chellappa · 2019
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NICO: A Dataset Towards Non-I.I.D. Image Classification
Y. He, Z. Shen, and P. Cui · 2019
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Continual reinforcement learning deployed in real-life using policy distillation and sim2real transfer
R. T. Kalifou, H. Caselles-Dupré, T. Lesort, T. Sun, N. Diaz-Rodriguez, and D. Filliat · 2019
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S. Kim, A. Coninx, and S. Doncieux · 2019
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Generative Models from the perspective of Continual Learning
T. Lesort, H. Caselles-Dupré, M. Garcia-Ortiz, J.-F. Goudou, and D. Filliat · 2019
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Marginal replay vs conditional replay for continual learning
T. Lesort, A. Gepperth, A. Stoian, and D. Filliat · 2019
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Deep unsupervised state representation learning with robotic priors: a robustness analysis
T. Lesort, M. Seurin, X. Li, N. Díaz-Rodríguez, and D. Filliat · 2019
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Training discriminative models to evaluate generative ones
T. Lesort, A. Stoian, J. Goudou, and D. Filliat · 2019
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Continual reinforcement learning in 3d non-stationary environments
V. Lomonaco, K. Desai, E. Culurciello, and D. Maltoni · 2019
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Continuous learning in single-incremental-task scenarios
D. Maltoni and V. Lomonaco · 2019
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Incremental learning techniques for semantic segmentation
U. Michieli and P. Zanuttigh · 2019
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Knowledge distillation for incremental learning in semantic segmentation, 2019
U. Michieli and P. Zanuttigh · 2019
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Learning and forgetting using reinforced bayesian change detection
V. Moens and A. Zénon · 2019
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Continual lifelong learning with neural networks: A review
G. I. Parisi, R. Kemker, J. L. Part, C. Kanan, and S. Wermter · 2019
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A comprehensive, application-oriented study of catastrophic forgetting in DNNs
B. Pfulb and A. Gepperth · 2019
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Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics
A. Raffin, A. Hill, K. R. Traoré, T. Lesort, N. Díaz-Rodríguez, and D. Filliat · 2019
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Learning to learn without forgetting by maximizing transfer and minimizing interference
M. Riemer, I. Cases, R. Ajemian, M. Liu, I. Rish, Y. Tu, , and G. Tesauro · 2019
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Closed-loop memory gan for continual learning
A. Rios and L. Itti · 2019
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Derivative-free online learning of inverse dynamics models
D. Romeres, M. Zorzi, R. Camoriano, S. Traversaro, and A. Chiuso · 2019
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Discorl: Continual reinforcement learning via policy distillation
R. Traoré, H. Caselles-Dupré, T. Lesort, T. Sun, G. Cai, N. D. Rodríguez, and D. Filliat · 2019
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A survey of zero-shot learning: Settings, methods, and applications
W. Wang, V. W. Zheng, H. Yu, and C. Miao · 2019
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