Fetching the paper…
Reading the bibliography…
While many works on Continual Learning have shown promising results for mitigating catastrophic forgetting, they have relied on supervised training.
The mnist database of handwritten digits
Yann LeCun · 1998
Earlier work this paper cites.
Anomaly detection over noisy data using learned probability distributions
Eleazar Eskin · 2000
Earlier work this paper cites.
Distance-based outliers: algorithms and applications
Edwin M Knorr, Raymond T Ng, and Vladimir Tucakov · 2000
Earlier work this paper cites.
Outlier detection using k-nearest neighbour graph
Ville Hautamaki, Ismo Karkkainen, and Pasi Franti · 2004
Earlier work this paper cites.
On-line unsupervised outlier detection using finite mixtures with discounting learning algorithms
Kenji Yamanishi, Jun-Ichi Takeuchi, Graham Williams, and Peter Milne · 2004
Earlier work this paper cites.
Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y Ng · 2011
Earlier work this paper cites.
Toward open set recognition
Walter J Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E Boult · 2012
Earlier work this paper cites.
An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Probability models for open set recognition
Walter J Scheirer, Lalit P Jain, and Terrance E Boult · 2014
Earlier work this paper cites.
Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Earlier work this paper cites.
A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
Earlier work this paper cites.
Deep generative dual memory network for continual learning
Nitin Kamra, Umang Gupta, and Yan Liu · 2017
Earlier work this paper cites.
Overcoming catastrophic forgetting in neural networks
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A Rusu, Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, et al · 2017
Cited alongside, same era.
Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and Rayadurgam Srikant · 2017
Cited alongside, same era.
Gradient episodic memory for continual learning
David Lopez-Paz and Marc’Aurelio Ranzato · 2017
Cited alongside, same era.
icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
Cited alongside, same era.
Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
Cited alongside, same era.
Mark Kliger and Shachar Fleishman · 2018
Later among the works it cites.
Adversarially learned one-class classifier for novelty detection
Mohammad Sabokrou, Mohammad Khalooei, Mahmood Fathy, and Ehsan Adeli · 2018
Later among the works it cites.
Memory replay gans: Learning to generate new categories without forgetting
Chenshen Wu, Luis Herranz, Xialei Liu, Joost van de Weijer, Bogdan Raducanu, et al · 2018
Later among the works it cites.
Continual lifelong learning with neural networks: A review
German I Parisi, Ronald Kemker, Jose L Part, Christopher Kanan, and Stefan Wermter · 2019
Later among the works it cites.
Continual unsupervised representation learning
Dushyant Rao et al · 2019
Later among the works it cites.
Experience replay for continual learning
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Han Xiao, Kashif Rasul, and Roland Vollgraf · 2017
Cited alongside, same era.
Lifelong learning with dynamically expandable networks
Jaehong Yoon, Eunho Yang, Jeongtae Lee, and Sung Ju Hwang · 2017
Cited alongside, same era.
Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
Cited alongside, same era.
Uncertainty in the variational information bottleneck
Alexander A Alemi, Ian Fischer, and Joshua V Dillon · 2018
Cited alongside, same era.
Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
Cited alongside, same era.
End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
Cited alongside, same era.
Generative ensembles for robust anomaly detection
Hyunsun Choi and Eric Jang · 2018
Cited alongside, same era.
David Rolnick, Arun Ahuja, Jonathan Schwarz, Timothy Lillicrap, and Gregory Wayne · 2019
Later among the works it cites.
Zero-shot out-of-distribution detection with feature correlations
Chandramouli S Sastry and Sageev Oore · 2019
Later among the works it cites.
Input complexity and out-of-distribution detection with likelihood-based generative models
Joan Serrà, David Álvarez, Vicenç Gómez, Olga Slizovskaia, José F Núñez, and Jordi Luque · 2019
Later among the works it cites.
Unsupervised progressive learning and the stam architecture
James Smith and Constantine Dovrolis · 2019
Later among the works it cites.
Incremental object learning from contiguous views
Stefan Stojanov, Samarth Mishra, Ngoc Anh Thai, Nikhil Dhanda, Ahmad Humayun, Chen Yu, Linda B Smith, and James M Rehg · 2019
Later among the works it cites.
Large scale incremental learning
Yue Wu, Yinpeng Chen, Lijuan Wang, Yuancheng Ye, Zicheng Liu, Yandong Guo, and Yun Fu · 2019
Later among the works it cites.
A continual learning survey: Defying forgetting in classification tasks
Matthias De Lange, Rahaf Alijundi, Marc Masana, Sarah Parisot, Xu Jia, Ales Leonardis, Gregory Slabaugh, and Tinne Tuytelaars · 2020
Later among the works it cites.
A neural dirichlet process mixture model for task-free continual learning
Soochan Lee, Junsoo Ha, Dongsu Zhang, and Gunhee Kim · 2020
Later among the works it cites.