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Machine learning has achieved remarkable success in many applications.
The hungarian method for the assignment problem
Harold W Kuhn · 1955
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Some methods for classification and analysis of multivariate observations
James MacQueen et al · 1967
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Learning internal representations by error propagation, 1985
David E Rumelhart, Geoffrey E Hinton, Ronald J Williams, et al · 1985
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Catastrophic interference in connectionist networks: The sequential learning problem
Michael McCloskey and Neal J Cohen · 1989
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Catastrophic interference in connectionist networks: The sequential learning problem
M. McCloskey and N. J. Cohen · 1989
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Interactive tandem networks and the sequential learning problem
Robert M French · 1995
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Regression shrinkage and selection via the lasso
Robert Tibshirani · 1996
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Metaplasticity: the plasticity of synaptic plasticity
Wickliffe C Abraham and Mark F Bear · 1996
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Gradient-based learning applied to document recognition
Yann LeCun, Léon Bottou, Yoshua Bengio, and Patrick Haffner · 1998
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Pattern recognition and machine learning
Christopher M Bishop and Nasser M Nasrabadi · 2006
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A tutorial on energy-based learning
Yann LeCun, Sumit Chopra, Raia Hadsell, M Ranzato, and Fujie Huang · 2006
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The relationship between precision-recall and roc curves
Jesse Davis and Mark Goadrich · 2006
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K-means++ the advantages of careful seeding
David Arthur and Sergei Vassilvitskii · 2007
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The elements of statistical learning: data mining, inference, and prediction
Trevor Hastie, Robert Tibshirani, and Jerome H Friedman · 2009
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Anomaly detection: A survey
Varun Chandola, Arindam Banerjee, and Vipin Kumar · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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The map equation
Martin Rosvall, Daniel Axelsson, and Carl T Bergstrom · 2009
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Bo Wu, and Andrew Y. Ng · 2011
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Reading digits in natural images with unsupervised feature learning
Yuval Netzer, Tao Wang, Adam Coates, Alessandro Bissacco, Baolin Wu, Andrew Y Ng, et al · 2011
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The caltech-ucsd birds-200-2011 dataset
Catherine Wah, Steve Branson, Peter Welinder, Pietro Perona, and Serge Belongie · 2011
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Scikit-learn: Machine learning in python
Fabian Pedregosa, Gaël Varoquaux, Alexandre Gramfort, Vincent Michel, Bertrand Thirion, Olivier Grisel, Mathieu Blondel, Peter Prettenhofer, Ron Weiss, Vincent Dubourg, et al · 2011
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Toward open set recognition
Walter J Scheirer, Anderson de Rezende Rocha, Archana Sapkota, and Terrance E Boult · 2012
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Distance-based image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 2013
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Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks
Dong-Hyun Lee et al · 2013
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Sinkhorn distances: Lightspeed computation of optimal transport
Marco Cuturi · 2013
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An extensive comparative study of cluster validity indices
Olatz Arbelaitz, Ibai Gurrutxaga, Javier Muguerza, Jesús M Pérez, and Iñigo Perona · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
Subhransu Maji, Esa Rahtu, Juho Kannala, Matthew Blaschko, and Andrea Vedaldi · 2013
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An empirical investigation of catastrophic forgetting in gradient-based neural networks
Ian J Goodfellow, Mehdi Mirza, Da Xiao, Aaron Courville, and Yoshua Bengio · 2013
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Probability models for open set recognition
Walter J Scheirer, Lalit P Jain, and Terrance E Boult · 2014
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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Describing textures in the wild
Mircea Cimpoi, Subhransu Maji, Iasonas Kokkinos, Sammy Mohamed, and Andrea Vedaldi · 2014
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Ward’s hierarchical agglomerative clustering method: which algorithms implement ward’s criterion?
Fionn Murtagh and Pierre Legendre · 2014
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Towards open world recognition
Abhijit Bendale and Terrance Boult · 2015
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Distilling the knowledge in a neural network
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean · 2015
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Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop
Fisher Yu, Ari Seff, Yinda Zhang, Shuran Song, Thomas Funkhouser, and Jianxiong Xiao · 2015
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Turkergaze: Crowdsourcing saliency with webcam based eye tracking
Pingmei Xu, Krista A Ehinger, Yinda Zhang, Adam Finkelstein, Sanjeev R Kulkarni, and Jianxiong Xiao · 2015
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Tiny imagenet visual recognition challenge
Yann Le and Xuan Yang · 2015
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2016
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Deep learning
Ian Goodfellow, Yoshua Bengio, Aaron Courville, and Yoshua Bengio · 2016
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Sparse representation-based open set recognition
He Zhang and Vishal M Patel · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Rethinking the inception architecture for computer vision
Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna · 2016
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Unsupervised deep embedding for clustering analysis
Junyuan Xie, Ross Girshick, and Ali Farhadi · 2016
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Regularization with stochastic transformations and perturbations for deep semi-supervised learning
Mehdi Sajjadi, Mehran Javanmardi, and Tolga Tasdizen · 2016
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Paying more attention to attention: Improving the performance of convolutional neural networks via attention transfer
Sergey Zagoruyko and Nikos Komodakis · 2016
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Confidence and certainty: distinct probabilistic quantities for different goals
Alexandre Pouget, Jan Drugowitsch, and Adam Kepecs · 2016
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Generative openmax for multi-class open set classification
ZongYuan Ge, Sergey Demyanov, Zetao Chen, and Rahil Garnavi · 2017
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Overcoming catastrophic forgetting in neural networks
J. Kirkpatrick, Razvan Pascanu, Neil C. Rabinowitz, et al · 2017
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Continual learning through synaptic intelligence
Friedemann Zenke, Ben Poole, and Surya Ganguli · 2017
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Encoder based lifelong learning
Amal Rannen, Rahaf Aljundi, Matthew B Blaschko, and Tinne Tuytelaars · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, A. Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
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Continual learning with deep generative replay
Hanul Shin, Jung Kwon Lee, Jaehong Kim, and Jiwon Kim · 2017
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Improved regularization of convolutional neural networks with cutout
Terrance DeVries and Graham W Taylor · 2017
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Imbalanced-learn: A python toolbox to tackle the curse of imbalanced datasets in machine learning
Guillaume Lemaître, Fernando Nogueira, and Christos K Aridas · 2017
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Engrams and circuits crucial for systems consolidation of a memory
Takashi Kitamura, Sachie K Ogawa, Dheeraj S Roy, Teruhiro Okuyama, Mark D Morrissey, Lillian M Smith, Roger L Redondo, and Susumu Tonegawa · 2017
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Conditional image synthesis with auxiliary classifier gans
Augustus Odena, Christopher Olah, and Jonathon Shlens · 2017
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Lifelong machine learning
Zhiyuan Chen and Bing Liu · 2018
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Enhancing the reliability of out-of-distribution image detection in neural networks
Shiyu Liang, Yixuan Li, and R Srikant · 2018
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A simple unified framework for detecting out-of-distribution samples and adversarial attacks
Kimin Lee, Kibok Lee, Honglak Lee, and Jinwoo Shin · 2018
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Learning to cluster in order to transfer across domains and tasks
Yen-Chang Hsu, Zhaoyang Lv, and Zsolt Kira · 2018
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Memory aware synapses: Learning what (not) to forget
Rahaf Aljundi, Francesca Babiloni, Mohamed Elhoseiny, Marcus Rohrbach, and Tinne Tuytelaars · 2018
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Riemannian walk for incremental learning: Understanding forgetting and intransigence
Arslan Chaudhry, Puneet K Dokania, Thalaiyasingam Ajanthan, and Philip HS Torr · 2018
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Variational continual learning
Cuong V Nguyen, Yingzhen Li, Thang D Bui, and Richard E Turner · 2018
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End-to-end incremental learning
Francisco M Castro, Manuel J Marín-Jiménez, et al · 2018
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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
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Fearnet: Brain-inspired model for incremental learning
Ronald Kemker and Christopher Kanan · 2018
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Progress & compress: A scalable framework for continual learning
Jonathan Schwarz, Wojciech Czarnecki, Jelena Luketina, Agnieszka Grabska-Barwinska, Yee Whye Teh, Razvan Pascanu, and Raia Hadsell · 2018
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Piggyback: Adapting a single network to multiple tasks by learning to mask weights
Arun Mallya, Dillon Davis, and Svetlana Lazebnik · 2018
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Overcoming catastrophic forgetting with hard attention to the task
Joan Serra, Didac Suris, Marius Miron, and Alexandros Karatzoglou · 2018
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Packnet: Adding multiple tasks to a single network by iterative pruning
Arun Mallya and Svetlana Lazebnik · 2018
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A survey on deep learning: Algorithms, techniques, and applications
Samira Pouyanfar, Saad Sadiq, Yilin Yan, Haiman Tian, Yudong Tao, Maria Presa Reyes, Mei-Ling Shyu, Shu-Ching Chen, and Sundaraja S Iyengar · 2018
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Glow: Generative flow with invertible 1x1 convolutions
Durk P Kingma and Prafulla Dhariwal · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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mixup: Beyond empirical risk minimization
Hongyi Zhang, Moustapha Cisse, Yann N Dauphin, and David Lopez-Paz · 2018
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Reducing network agnostophobia
Akshay Raj Dhamija, Manuel Günther, and Terrance Boult · 2018
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Open set learning with counterfactual images
Lawrence Neal, Matthew Olson, Xiaoli Fern, Weng-Keen Wong, and Fuxin Li · 2018
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Robust classification with convolutional prototype learning
Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, and Cheng-Lin Liu · 2018
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Places: A 10 million image database for scene recognition
Bolei Zhou, Àgata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2018
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Deep clustering for unsupervised learning of visual features
Mathilde Caron, Piotr Bojanowski, Armand Joulin, and Matthijs Douze · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin A Raffel, Ekin Dogus Cubuk, and Ian Goodfellow · 2018
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Learning without forgetting
Zhizhong Li and Derek Hoiem · 2018
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Re-evaluating continual learning scenarios: A categorization and case for strong baselines
Yen-Chang Hsu, Y. Liu, and Z. Kira · 2018
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Incremental adaptive learning vector quantization for character recognition with continuous style adaptation
Yuan-Yuan Shen and Cheng-Lin Liu · 2018
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Using self-supervised learning can improve model robustness and uncertainty
Dan Hendrycks, Mantas Mazeika, Saurav Kadavath, and Dawn Song · 2019
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Deep anomaly detection with outlier exposure
Dan Hendrycks, Mantas Mazeika, and Thomas Dietterich · 2019
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C2ae: Class conditioned auto-encoder for open-set recognition
Poojan Oza and Vishal M Patel · 2019
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Multi-class classification without multi-class labels
Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, and Zsolt Kira · 2019
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Learning to discover novel visual categories via deep transfer clustering
Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2019
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Uncertainty-based continual learning with adaptive regularization
Hongjoon Ahn, Sungmin Cha, Donggyu Lee, and Taesup Moon · 2019
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Continual learning of context-dependent processing in neural networks
Guanxiong Zeng, Yang Chen, Bo Cui, and Shan Yu · 2019
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Learning without memorizing
Prithviraj Dhar, Rajat Vikram Singh, Kuan-Chuan Peng, et al · 2019
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M2kd: Multi-model and multi-level knowledge distillation for incremental learning
Peng Zhou, Long Mai, Jianming Zhang, Ning Xu, Zuxuan Wu, and Larry S Davis · 2019
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Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and D. Lin · 2019
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Large scale incremental learning
Y. Wu, Yan-Jia Chen, Lijuan Wang, et al · 2019
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Il2m: Class incremental learning with dual memory
Eden Belouadah and Adrian Popescu · 2019
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Incremental learning using conditional adversarial networks
Ye Xiang, Ying Fu, Pan Ji, and Hua Huang · 2019
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A probabilistic challenge for object detection
Niko Suenderhauf, Feras Dayoub, David Hall, John Skinner, Haoyang Zhang, Gustavo Carneiro, and Peter Corke · 2019
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Deep transfer learning for multiple class novelty detection
Pramuditha Perera and Vishal M Patel · 2019
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Classification-reconstruction learning for open-set recognition
Ryota Yoshihashi, Wen Shao, Rei Kawakami, Shaodi You, Makoto Iida, and Takeshi Naemura · 2019
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Residual flows for invertible generative modeling
Ricky TQ Chen, Jens Behrmann, David K Duvenaud, and Jörn-Henrik Jacobsen · 2019
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The herbarium challenge 2019 dataset
Kiat Chuan Tan, Yulong Liu, Barbara Ambrose, Melissa Tulig, and Serge Belongie · 2019
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Continual learning in neural networks
Rahaf Aljundi · 2019
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Overcoming catastrophic forgetting with unlabeled data in the wild
Kibok Lee, Kimin Lee, Jinwoo Shin, and Honglak Lee · 2019
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Three scenarios for continual learning
Gido M. van de Ven and A. Tolias · 2019
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An introduction to variational autoencoders
Diederik P. Kingma and Max Welling · 2019
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Relational knowledge distillation
Wonpyo Park, Dongju Kim, Yan Lu, and Minsu Cho · 2019
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Privacy and security of big data in ai systems: A research and standards perspective
Saharnaz Dilmaghani, Matthias R Brust, Grégoire Danoy, Natalia Cassagnes, Johnatan Pecero, and Pascal Bouvry · 2019
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Accelerated first-order optimization algorithms for machine learning
Huan Li, Cong Fang, and Zhouchen Lin · 2020
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Towards robust pattern recognition: A review
Xu-Yao Zhang, Cheng-Lin Liu, and Ching Y Suen · 2020
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Energy-based out-of-distribution detection
Weitang Liu, Xiaoyun Wang, John Owens, and Yixuan Li · 2020
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Hybrid models for open set recognition
Hongjie Zhang, Ang Li, Jie Guo, and Yanwen Guo · 2020
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Convolutional prototype network for open set recognition
Hong-Ming Yang, Xu-Yao Zhang, Fei Yin, Qing Yang, and Cheng-Lin Liu · 2020
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Automatically discovering and learning new visual categories with ranking statistics
Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Andrea Vedaldi, and Andrew Zisserman · 2020
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Continual learning with node-importance based adaptive group sparse regularization
Sangwon Jung, Hongjoon Ahn, Sungmin Cha, and Taesup Moon · 2020
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Orthogonal gradient descent for continual learning
Mehrdad Farajtabar, Navid Azizan, Alex Mott, and Ang Li · 2020
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Gradient projection memory for continual learning
Gobinda Saha, Isha Garg, and Kaushik Roy · 2020
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Class-incremental learning via deep model consolidation
Junting Zhang, Jie Zhang, Shalini Ghosh, Dawei Li, Serafettin Tasci, Larry Heck, Heming Zhang, and C-C Jay Kuo · 2020
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More classifiers, less forgetting: A generic multi-classifier paradigm for incremental learning
Yu Liu, Sarah Parisot, Gregory Slabaugh, Xu Jia, Ales Leonardis, and Tinne Tuytelaars · 2020
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Podnet: Pooled outputs distillation for small-tasks incremental learning
Arthur Douillard, Matthieu Cord, Charles Ollion, Thomas Robert, and Eduardo Valle · 2020
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Dark experience for general continual learning: a strong, simple baseline
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and Simone Calderara · 2020
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Gdumb: A simple approach that questions our progress in continual learning
Ameya Prabhu, Philip Torr, and Puneet Dokania · 2020
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Maintaining discrimination and fairness in class incremental learning
Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, and Shu-Tao Xia · 2020
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Scail: Classifier weights scaling for class incremental learning
Eden Belouadah and Adrian Popescu · 2020
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Generative feature replay for class-incremental learning
Xialei Liu, Chenshen Wu, Mikel Menta, Luis Herranz, Bogdan Raducanu, Andrew D Bagdanov, Shangling Jui, and Joost van de Weijer · 2020
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Remind your neural network to prevent catastrophic forgetting
Tyler L Hayes, Kushal Kafle, Robik Shrestha, Manoj Acharya, and Christopher Kanan · 2020
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Latent replay for real-time continual learning
Lorenzo Pellegrini, Gabriele Graffieti, Vincenzo Lomonaco, and Davide Maltoni · 2020
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Brain-inspired replay for continual learning with artificial neural networks
Gido M van de Ven, Hava T Siegelmann, and Andreas S Tolias · 2020
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Memory-efficient incremental learning through feature adaptation
Ahmet Iscen, Jeffrey Zhang, Svetlana Lazebnik, and Cordelia Schmid · 2020
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Semantic drift compensation for class-incremental learning
Lu Yu, Bartlomiej Twardowski, Xialei Liu, Luis Herranz, Kai Wang, Yongmei Cheng, Shangling Jui, and Joost van de Weijer · 2020
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Clinical applications of continual learning machine learning
Cecilia S Lee and Aaron Y Lee · 2020
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Recent advances in open set recognition: A survey
Chuanxing Geng, Sheng-jun Huang, and Songcan Chen · 2020
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Deep residual flow for out of distribution detection
Ev Zisselman and Aviv Tamar · 2020
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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Csi: Novelty detection via contrastive learning on distributionally shifted instances
Jihoon Tack, Sangwoo Mo, Jongheon Jeong, and Jinwoo Shin · 2020
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Randaugment: Practical automated data augmentation with a reduced search space
Ekin D Cubuk, Barret Zoph, Jonathon Shlens, and Quoc V Le · 2020
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Conditional gaussian distribution learning for open set recognition
Xin Sun, Zhenning Yang, Chi Zhang, Keck-Voon Ling, and Guohao Peng · 2020
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Why normalizing flows fail to detect out-of-distribution data
Polina Kirichenko, Pavel Izmailov, and Andrew G Wilson · 2020
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Learning open set network with discriminative reciprocal points
Guangyao Chen, Limeng Qiao, Yemin Shi, Peixi Peng, Jia Li, Tiejun Huang, Shiliang Pu, and Yonghong Tian · 2020
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Unsupervised learning of visual features by contrasting cluster assignments
Mathilde Caron, Ishan Misra, Julien Mairal, Priya Goyal, Piotr Bojanowski, and Armand Joulin · 2020
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Supervised contrastive learning
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Nicholas Carlini, Zizhao Zhang, Han Zhang, Colin A Raffel, Ekin Dogus Cubuk, Alexey Kurakin, and Chun-Liang Li · 2020
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Federated learning: Challenges, methods, and future directions
Tian Li, Anit Kumar Sahu, Ameet Talwalkar, and Virginia Smith · 2020
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Dreaming to distill: Data-free knowledge transfer via deepinversion
Hongxu Yin, Pavlo Molchanov, Jose M Alvarez, Zhizhong Li, Arun Mallya, Derek Hoiem, Niraj K Jha, and Jan Kautz · 2020
Cited alongside, same era.
Topology-preserving class-incremental learning
Xiaoyu Tao, Xinyuan Chang, Xiaopeng Hong, Xing Wei, and Yihong Gong · 2020
Cited alongside, same era.
Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
Cited alongside, same era.
Generalisation guarantees for continual learning with orthogonal gradient descent
Mehdi Abbana Bennani and Masashi Sugiyama · 2020
Cited alongside, same era.
Towards fairness in visual recognition: Effective strategies for bias mitigation
Zeyu Wang, Klint Qinami, Ioannis Christos Karakozis, Kyle Genova, Prem Nair, Kenji Hata, and Olga Russakovsky · 2020
Cited alongside, same era.
Hierarchical decomposition of prompt-based continual learning: Rethinking obscured sub-optimality
Liyuan Wang, Jingyi Xie, Xingxing Zhang, Mingyi Huang, Hang Su, and Jun Zhu · 2023
Later among the works it cites.
Introducing language guidance in prompt-based continual learning
Muhammad Gul Zain Ali Khan, Muhammad Ferjad Naeem, Luc Van Gool, Didier Stricker, Federico Tombari, and Muhammad Zeshan Afzal · 2023
Later among the works it cites.
Prompt gradient projection for continual learning
Jingyang Qiao, Xin Tan, Chengwei Chen, Yanyun Qu, Yong Peng, Yuan Xie, et al · 2023
Later among the works it cites.
Orthogonal subspace learning for language model continual learning
Xiao Wang, Tianze Chen, Qiming Ge, Han Xia, Rong Bao, Rui Zheng, Qi Zhang, Tao Gui, and Xuanjing Huang · 2023
Later among the works it cites.
Progressive prompts: Continual learning for language models
Anastasia Razdaibiedina, Yuning Mao, Rui Hou, Madian Khabsa, Mike Lewis, and Amjad Almahairi · 2023
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Rui Huang and Yixuan Li · 2021
Cited alongside, same era.
Learning placeholders for open-set recognition
Da-Wei Zhou, Han-Jia Ye, and De-Chuan Zhan · 2021
Cited alongside, same era.
Opengan: Open-set recognition via open data generation
Shu Kong and Deva Ramanan · 2021
Cited alongside, same era.
Class anchor clustering: A loss for distance-based open set recognition
Dimity Miller, Niko Sunderhauf, Michael Milford, and Feras Dayoub · 2021
Cited alongside, same era.
Adversarial reciprocal points learning for open set recognition
Guangyao Chen, Peixi Peng, Xiangqian Wang, and Yonghong Tian · 2021
Cited alongside, same era.
Autonovel: Automatically discovering and learning novel visual categories
Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Andrea Vedaldi, and Andrew Zisserman · 2021
Cited alongside, same era.
Neighborhood contrastive learning for novel class discovery
Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, and Nicu Sebe · 2021
Cited alongside, same era.
Later among the works it cites.
Continual pre-training of language models
Zixuan Ke, Yijia Shao, Haowei Lin, Tatsuya Konishi, Gyuhak Kim, and Bing Liu · 2023
Later among the works it cites.
A survey on learning to reject
Xu-Yao Zhang, Guo-Sen Xie, Xiuli Li, Tao Mei, and Cheng-Lin Liu · 2023
Later among the works it cites.
Continual learning: Applications and the road forward
Eli Verwimp, Shai Ben-David, Matthias Bethge, Andrea Cossu, Alexander Gepperth, Tyler L Hayes, Eyke Hüllermeier, Christopher Kanan, Dhireesha Kudithipudi, Christoph H Lampert, et al · 2023
Later among the works it cites.
A comprehensive survey of continual learning: Theory, method and application
Liyuan Wang, Xingxing Zhang, Hang Su, and Jun Zhu · 2023
Later among the works it cites.
Open-world machine learning: applications, challenges, and opportunities
Jitendra Parmar, Satyendra Chouhan, Vaskar Raychoudhury, and Santosh Rathore · 2023
Later among the works it cites.
OpenAI · 2023
Later among the works it cites.
Exploring ai ethics of chatgpt: A diagnostic analysis
Terry Yue Zhuo, Yujin Huang, Chunyang Chen, and Zhenchang Xing · 2023
Later among the works it cites.
Ai autonomy: Self-initiated open-world continual learning and adaptation
Bing Liu, Sahisnu Mazumder, Eric Robertson, and Scott Grigsby · 2023
Later among the works it cites.
Mixture outlier exposure: Towards out-of-distribution detection in fine-grained environments
Jingyang Zhang, Nathan Inkawhich, Randolph Linderman, Yiran Chen, and Hai Li · 2023
Later among the works it cites.
Feed two birds with one scone: Exploiting wild data for both out-of-distribution generalization and detection
Haoyue Bai, Gregory Canal, Xuefeng Du, Jeongyeol Kwon, Robert D Nowak, and Yixuan Li · 2023
Later among the works it cites.
Openood v1. 5: Enhanced benchmark for out-of-distribution detection
Jingyang Zhang, Jingkang Yang, Pengyun Wang, Haoqi Wang, Yueqian Lin, Haoran Zhang, Yiyou Sun, Xuefeng Du, Kaiyang Zhou, Wayne Zhang, et al · 2023
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