Fetching the paper…
Reading the bibliography…
As a fundamental issue in lifelong learning, catastrophic forgetting is directly caused by inaccessible historical data; accordingly, if the data (information) were memorized perfectly, no forgetting should be expected.
Catastrophic interference in connectionist networks: The sequential learning problem
M. McCloskey and N. Cohen · 1989
Earlier work this paper cites.
Lifelong robot learning
S. Thrun and T. Mitchell · 1995
Earlier work this paper cites.
Adult neurogenesis and neural stem cells of the central nervous system in mammals
P. Taupin and F. Gage · 2002
Earlier work this paper cites.
Sleep transforms the cerebral trace of declarative memories
S. Gais, G. Albouy, M. Boly, T. Dang-Vu, A. Darsaud, M. Desseilles, G. Rauchs, M. Schabus, V. Sterpenich, G. Vandewalle, et al · 2007
Earlier work this paper cites.
The energy of graphs and matrices
V. Nikiforov · 2007
Earlier work this paper cites.
Automated flower classification over a large number of classes
M. Nilsback and A. Zisserman · 2008
Earlier work this paper cites.
Cat head detection-how to effectively exploit shape and texture features
W. Zhang, J. Sun, and X. Tang · 2008
Earlier work this paper cites.
Deep learning of representations for unsupervised and transfer learning
Y. Bengio · 2012
Earlier work this paper cites.
Overfeat: Integrated recognition, localization and detection using convolutional networks
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2013
Earlier work this paper cites.
Decaf: A deep convolutional activation feature for generic visual recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
Earlier work this paper cites.
Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2014
Earlier work this paper cites.
Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
Earlier work this paper cites.
Conditional generative adversarial nets
M. Mirza and S. Osindero · 2014
Earlier work this paper cites.
How transferable are features in deep neural networks?
J. Yosinski, J. Clune, Y. Bengio, and H. Lipson · 2014
Earlier work this paper cites.
Visualizing and understanding convolutional networks
M. Zeiler and R. Fergus · 2014
Earlier work this paper cites.
Learning deep features for scene recognition using places database
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva · 2014
Earlier work this paper cites.
Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Z. Liu, P. Luo, X. Wang, and X. Tang · 2015
Earlier work this paper cites.
Learning transferable features with deep adaptation networks
M. Long, Y. Cao, J. Wang, and M. Jordan · 2015
Earlier work this paper cites.
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, et al · 2015
Earlier work this paper cites.
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
Earlier work this paper cites.
Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation
J. Cheng, W. Yang, M. Huang, W. Huang, J. Jiang, Y. Zhou, R. Yang, J. Zhao, Y. Feng, and Q. Feng · 2016
Earlier work this paper cites.
A learned representation for artistic style
V. Dumoulin, J. Shlens, and M. Kudlur · 2016
Earlier work this paper cites.
Network dissection: Quantifying interpretability of deep visual representations
D. Bau, B. Zhou, A. Khosla, A. Oliva, and A. Torralba · 2017
Earlier work this paper cites.
Stylebank: An explicit representation for neural image style transfer
D. Chen, L. Yuan, J. Liao, N. Yu, and G. Hua · 2017
Earlier work this paper cites.
Exploring the structure of a real-time, arbitrary neural artistic stylization network
G. Ghiasi, H. Lee, M. Kudlur, V. Dumoulin, and J. Shlens · 2017
Earlier work this paper cites.
Neuroscience-inspired artificial intelligence
D. Hassabis, D. Kumaran, C. Summerfield, and M. Botvinick · 2017
Earlier work this paper cites.
GANs trained by a two time-scale update rule converge to a local Nash equilibrium
M. Heusel, H. Ramsauer, T. Unterthiner, B. Nessler, and S. Hochreiter · 2017
Earlier work this paper cites.
Arbitrary style transfer in real-time with adaptive instance normalization
X. Huang and S. Belongie · 2017
Earlier work this paper cites.
Progressive growing of gans for improved quality, stability, and variation
T. Karras, T. Aila, S. Laine, and J. Lehtinen · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, R. Pascanu, N. Rabinowitz, J. Veness, G. Desjardins, A. Rusu, K. Milan, J. Quan, T. Ramalho, A. Grabska-Barwinska, et al · 2017
Earlier work this paper cites.
Photo-realistic single image super-resolution using a generative adversarial network
C. Ledig, L. Theis, F. Huszár, J. Caballero, A. Cunningham, A. Acosta, A. Aitken, A. Tejani, J. Totz, Z. Wang, et al · 2017
Earlier work this paper cites.
Universal style transfer via feature transforms
Y. Li, C. Fang, J. Yang, Z. Wang, X. Lu, and M. Yang · 2017
Earlier work this paper cites.
Learning without forgetting
Z. Li and D. Hoiem · 2017
Cited alongside, same era.
Label efficient learning of transferable representations acrosss domains and tasks
Z. Luo, Y. Zou, J. Hoffman, and L. Fei-Fei · 2017
Cited alongside, same era.
Variational continual learning
C. Nguyen, Y. Li, T. Bui, and R. Turner · 2017
Cited alongside, same era.
iCaRL: Incremental classifier and representation learning
S. Rebuffi, A. Kolesnikov, G. Sperl, and C. Lampert · 2017
Cited alongside, same era.
Continual learning in generative adversarial nets
A. Seff, A. Beatson, D. Suo, and H. Liu · 2017
Cited alongside, same era.
Continual learning with deep generative replay
H. Shin, J. Lee, J. Kim, and J. Kim · 2017
Cited alongside, same era.
Memory replay GANs: Learning to generate new categories without forgetting
C. Wu, L. Herranz, X. Liu, J. van de Weijer, B. Raducanu, et al · 2018
Later among the works it cites.
Taskonomy: Disentangling task transfer learning
A. Zamir, A. Sax, W. Shen, L. Guibas, J. Malik, and S. Savarese · 2018
Later among the works it cites.
Attribute manipulation generative adversarial networks for fashion images
K. Ak, J. Lim, J. Tham, and A. Kassim · 2019
Later among the works it cites.
Transfer fine-tuning: A BERT case study
Y. Arase and J. Tsujii · 2019
Later among the works it cites.
Transfer learning from pre-trained bert for pronoun resolution
X. Bao and Q. Qiao · 2019
Later among the works it cites.
Robust classification
D. Bertsimas, J. Dunn, C. Pawlowski, and Y. Zhuo · 2019
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Incremental learning of object detectors without catastrophic forgetting
K. Shmelkov, C. Schmid, and K. Alahari · 2017
Cited alongside, same era.
A domain based approach to social relation recognition
Q. Sun, B. Schiele, and M. Fritz · 2017
Cited alongside, same era.
Zm-net: Real-time zero-shot image manipulation network
H. Wang, X. Liang, H. Zhang, D. Yeung, and E. Xing · 2017
Cited alongside, same era.
The effectiveness of data augmentation in image classification using deep learning
J. Wang and L. Perez · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
F. Zenke, B. Poole, and S. Ganguli · 2017
Cited alongside, same era.
GAN dissection: Visualizing and understanding generative adversarial networks
D. Bau, J. Zhu, H. Strobelt, B. Zhou, J. Tenenbaum, W. Freeman, and A. Torralba · 2018
Cited alongside, same era.
Later among the works it cites.
Large scale GAN training for high fidelity natural image synthesis
A. Brock, J. Donahue, and K. Simonyan · 2019
Later among the works it cites.
Everybody dance now
C. Chan, S. Ginosar, T. Zhou, and A. Efros · 2019
Later among the works it cites.
Mind2mind: transfer learning for GANs
Y. Frégier and J. Gouray · 2019
Later among the works it cites.
C. Han, K. Murao, T. Noguchi, Y. Kawata, F. Uchiyama, L. Rundo, H. Nakayama, and S. Satoh · 2019
Later among the works it cites.
A style-based generator architecture for generative adversarial networks
T. Karras, S. Laine, and T. Aila · 2019
Later among the works it cites.
Analyzing and improving the image quality of stylegan
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila · 2019
Later among the works it cites.
MelGAN: Generative adversarial networks for conditional waveform synthesis
K. Kumar, R. Kumar, T. de Boissiere, L. Gestin, W. Teoh, J. Sotelo, A. de Brebisson, Y. Bengio, and A. Courville · 2019
Later among the works it cites.
Class-based styling: Real-time localized style transfer with semantic segmentation
L. Kurzman, D. Vazquez, and I. Laradji · 2019
Later among the works it cites.
Generative models from the perspective of continual learning
T. Lesort, H. Caselles-Dupré, M. Garcia-Ortiz, A. Stoian, and D. Filliat · 2019
Later among the works it cites.
HUBERT untangles BERT to improve transfer across NLP tasks
M. Moradshahi, H. Palangi, M. Lam, P. Smolensky, and J. Gao · 2019
Later among the works it cites.
A BERT-based transfer learning approach for hate speech detection in online social media
M. Mozafari, R. Farahbakhsh, and N. Crespi · 2019
Later among the works it cites.
Unified probabilistic deep continual learning through generative replay and open set recognition
M. Mundt, S. Majumder, I. Pliushch, and V. Ramesh · 2019
Later among the works it cites.
Image generation from small datasets via batch statistics adaptation
A. Noguchi and T. Harada · 2019
Later among the works it cites.
Learning to remember: A synaptic plasticity driven framework for continual learning
O. Ostapenko, M. Puscas, T. Klein, P. Jahnichen, and M. Nabi · 2019
Later among the works it cites.
Continual lifelong learning with neural networks: A review
G. Parisi, R. Kemker, J. Part, C. Kanan, and S. Wermter · 2019
Later among the works it cites.
Semantic image synthesis with spatially-adaptive normalization
T. Park, M. Liu, T. Wang, and J. Zhu · 2019
Later among the works it cites.
Y. Peng, S. Yan, and Z. Lu · 2019
Later among the works it cites.
Three scenarios for continual learning
G. van de Ven and A. Tolias · 2019
Later among the works it cites.
Open event extraction from online text using a generative adversarial network
R. Wang, D. Zhou, and Y. He · 2019
Later among the works it cites.
Large scale incremental learning
Y. Wu, Y. Chen, L. Wang, Y. Ye, Z. Liu, Y. Guo, and Y. Fu · 2019
Later among the works it cites.
Incremental learning using conditional adversarial networks
Y. Xiang, Y. Fu, P. Ji, and H. Huang · 2019
Later among the works it cites.
R. Yamamoto, E. Song, and J. Kim · 2019
Later among the works it cites.
Continual learning of context-dependent processing in neural networks
G. Zeng, Y. Chen, B. Cui, and S. Yu · 2019
Later among the works it cites.
Lifelong GAN: Continual learning for conditional image generation
M. Zhai, L. Chen, F. Tung, J. He, M. Nawhal, and G. Mori · 2019
Later among the works it cites.
Infinite brain MR images: PGGAN-based data augmentation for tumor detection
C. Han, L. Rundo, R. Araki, Y. Furukawa, G. Mauri, H. Nakayama, and H. Hayashi · 2020
Closest in time.
K-adapter: Infusing knowledge into pre-trained models with adapters
R. Wang, D. Tang, N. Duan, Z. Wei, X. Huang, C. Cao, D. Jiang, and M. Zhou · 2020
Closest in time.
On leveraging pretrained GANs for generation with limited data
M. Zhao, Y. Cong, and L. Carin · 2020
Closest in time.