Fetching the paper…
Reading the bibliography…
A major open problem on the road to artificial intelligence is the development of incrementally learning systems that learn about more and more concepts over time from a stream of data.
Catastrophic interference in connectionist networks: The sequential learning problem
M. McCloskey and N. J. Cohen · 1989
Earlier work this paper cites.
Catastrophic interference in connectionist networks: Can it be predicted, can it be prevented?
R. M. French · 1993
Earlier work this paper cites.
Catastrophic forgetting, rehearsal and pseudorehearsal
A. V. Robins · 1995
Earlier work this paper cites.
Avoiding catastrophic forgetting by coupling two reverberating neural networks
B. Ans and S. Rousset · 1997
Earlier work this paper cites.
Gradient-based learning applied to document recognition
Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner · 1998
Earlier work this paper cites.
Catastrophic forgetting in connectionist networks
R. M. French · 1999
Earlier work this paper cites.
Learn++: an incremental learning algorithm for supervised neural networks
R. Polikar, L. Upda, S. S. Upda, and V. Honavar · 2001
Earlier work this paper cites.
Open set face recognition using transduction
F. Li and H. Wechsler · 2005
Earlier work this paper cites.
Catastophic forgetting in neural networks
O.-M. Moe-Helgesen and H. Stranden · 2005
Earlier work this paper cites.
Learning multiple layers of features from tiny images
A. Krizhevsky · 2009
Earlier work this paper cites.
Herding dynamical weights to learn
M. Welling · 2009
Earlier work this paper cites.
Metric learning for large scale image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2012
Earlier work this paper cites.
Representation learning: A review and new perspectives
Y. Bengio, A. Courville, and P. Vincent · 2013
Earlier work this paper cites.
NEIL: Extracting visual knowledge from web data
X. Chen, A. Shrivastava, and A. Gupta · 2013
Cited alongside, same era.
Sparse subspace clustering: Algorithm, theory, and applications
E. Elhamifar and R. Vidal · 2013
Cited alongside, same era.
Attribute-based classification for zero-shot visual object categorization
C. H. Lampert, H. Nickisch, and S. Harmeling · 2013
Cited alongside, same era.
Distance-based image classification: Generalizing to new classes at near-zero cost
T. Mensink, J. Verbeek, F. Perronnin, and G. Csurka · 2013
Cited alongside, same era.
Towards open set recognition
W. J. Scheirer, A. Rocha, A. Sapkota, and T. E. Boult · 2013
Cited alongside, same era.
Enriching visual knowledge bases via object discovery and segmentation
X. Chen, A. Shrivastava, and A. Gupta · 2014
Cited alongside, same era.
Towards open world recognition
A. Bendale and T. Boult · 2015
Later among the works it cites.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2015
Later among the works it cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Later among the works it cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2015
Later among the works it cites.
Curriculum learning of multiple tasks
A. Pentina, V. Sharmanska, and C. H. Lampert · 2015
Later among the works it cites.
Classifier adaptation at prediction time
A. Royer and C. H. Lampert · 2015
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Learning everything about anything: Webly-supervised visual concept learning
S. K. Divvala, A. Farhadi, and C. Guestrin · 2014
Cited alongside, same era.
An empirical investigation of catastrophic forgeting in gradient-based neural networks
I. J. Goodfellow, M. Mirza, D. Xiao, A. Courville, and Y. Bengio · 2014
Cited alongside, same era.
Distilling the knowledge in a neural network
G. Hinton, O. Vinyals, and J. Dean · 2014
Cited alongside, same era.
Data-driven exemplar model selection
I. Misra, A. Shrivastava, and M. Hebert · 2014
Cited alongside, same era.
Incremental learning of NCM forests for large-scale image classification
M. Ristin, M. Guillaumin, J. Gall, and L. Van Gool · 2014
Cited alongside, same era.
Dropout: a simple way to prevent neural networks from overfitting
N. Srivastava, G. E. Hinton, A. Krizhevsky, I. Sutskever, and R. Salakhutdinov · 2014
Cited alongside, same era.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Later among the works it cites.
Learning without forgetting
Z. Li and D. Hoiem · 2016
Closest in time.
Cross-stitch networks for multi-task learning
I. Misra, A. Shrivastava, A. Gupta, and M. Hebert · 2016
Closest in time.
A. A. Rusu, N. C. Rabinowitz, G. Desjardins, H. Soyer, J. Kirkpatrick, K. Kavukcuoglu, R. Pascanu, and R. Hadsell · 2016
Closest in time.
Convolutional neural fabrics
S. Saxena and J. Verbeek · 2016
Closest in time.
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, et al · 2017
Closest in time.
Encoder based lifelong learning
A. Rannen Triki, R. Aljundi, M. B. Blaschko, and T. Tuytelaars · 2017
Closest in time.