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This paper identifies the flaws in existing open-world learning approaches and attempts to provide a complete picture in the form of \textbf{True Open-World Learning}.
Herding dynamical weights to learn
Max Welling · 2009
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Metric learning for large scale image classification: Generalizing to new classes at near-zero cost
Thomas Mensink, Jakob Verbeek, Florent Perronnin, and Gabriela Csurka · 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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Towards open world recognition
Abhijit Bendale and Terrance Boult · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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Towards open set deep networks
Abhijit Bendale and Terrance E. Boult · 2016
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High-dimensional and large-scale anomaly detection using a linear one-class svm with deep learning
Sarah M. Erfani, Sutharshan Rajasegarar, Shanika Karunasekera, and Christopher Leckie · 2016
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Unsupervised learning of visual representations by solving jigsaw puzzles
Mehdi Noroozi and Paolo Favaro · 2016
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Colorful image colorization
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2016
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A baseline for detecting misclassified and out-of-distribution examples in neural networks
Dan Hendrycks and Kevin Gimpel · 2017
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Colorization as a proxy task for visual understanding
Gustav Larsson, Michael Maire, and Gregory Shakhnarovich · 2017
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Representation learning by learning to count
Mehdi Noroozi, Hamed Pirsiavash, and Paolo Favaro · 2017
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icarl: Incremental classifiers and representation learning
Sylverstre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H. Lampert · 2017
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The extreme value machine
Ethan M Rudd, Lalit P Jain, Walter J Scheirer, and Terrance E Boult · 2017
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DOC: Deep open classification of text documents
Lei Shu, Hu Xu, and Bing Liu · 2017
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Split-brain autoencoders: Unsupervised learning by cross-channel prediction
Richard Zhang, Phillip Isola, and Alexei A. Efros · 2017
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Deesil: Deep-shallow incremental learning
Eden Belouadah and Adrian Popescu · 2018
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A systematic study of the class imbalance problem in convolutional neural networks
Mateusz Buda, Atsuto Maki, and Maciej A. Mazurowski · 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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End-to-end incremental learning
Francisco M. Castro, Manuel J Marín-Jiménez, Nicolás Guil, Cordelia Schmid, and Karteek Alahari · 2018
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Reducing network agnostophobia
Akshay Raj Dhamija, Manuel Günther, and Terrance Boult · 2018
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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 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 without forgetting
Zhizhong Li and Derek Hoiem · 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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Improvements to context based self-supervised learning
T. Nathan Mundhenk, Daniel Ho, and Barry Y. Chen · 2018
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Boosting self-supervised learning via knowledge transfer
Mehdi Noroozi, Ananth Vinjimoor, Paolo Favaro, and Hamed Pirsiavash · 2018
Cited alongside, same era.
Places: A 10 million image database for scene recognition
Bolei Zhou, Agata Lapedriza, Aditya Khosla, Aude Oliva, and Antonio Torralba · 2018
Scan: Learning to classify images without labels
Wouter Van Gansbeke, Simon Vandenhende, Stamatios Georgoulis, Marc Proesmans, and Luc Van Gool · 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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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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Lifelong machine learning with deep streaming linear discriminant analysis
Tyler L. Hayes and Christopher Kanan · 2020
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Incremental learning in online scenario
Jiangpeng He, Runyu Mao, Zeman Shao, and Fengqing Zhu · 2020
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Momentum contrast for unsupervised visual representation learning
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Il2m: Class incremental learning with dual memory
Eden Belouadah and Adrian Popescu · 2019
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Unsupervised pre-training of image features on non-curated data
Mathilde Caron, Piotr Bojanowski, Julien Mairal, and Armand Joulin · 2019
Cited alongside, same era.
Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
Cited alongside, same era.
Learning to discover novel visual categories via deep transfer clustering
Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2019
Cited alongside, same era.
Learning a unified classifier incrementally via rebalancing
Saihui Hou, Xinyu Pan, Chen Change Loy, Zilei Wang, and Dahua Lin · 2019
Cited alongside, same era.
Invariant information clustering for unsupervised image classification and segmentation
Xu Ji, João F Henriques, and Andrea Vedaldi · 2019
Cited alongside, same era.
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 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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Steering self-supervised feature learning beyond local pixel statistics
Simon Jenni, Hailin Jin, and Paolo Favaro · 2020
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Self-supervised visual feature learning with deep neural networks: A survey
Longlong Jing and Yingli Tian · 2020
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The open images dataset v4: Unified image classification, object detection, and visual relationship detection at scale
Alina Kuznetsova, Hassan Rom, Neil Alldrin, Jasper Uijlings, Ivan Krasin, Jordi Pont-Tuset, Shahab Kamali, Stefan Popov, Matteo Malloci, Alexander Kolesnikov, Tom Duerig, and Vittorio Ferrari · 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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Mnemonics training: Multi-class incremental learning without forgetting
Yaoyao Liu, Yuting Su, An-An Liu, Bernt Schiele, and Qianru Sun · 2020
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An appraisal of incremental learning methods
Yong Luo, Liancheng Yin, Wenchao Bai, and Keming Mao · 2020
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Class-incremental learning: survey and performance evaluation
Marc Masana, Xialei Liu, Bartlomiej Twardowski, Mikel Menta, Andrew D. Bagdanov, and Joost van de Weijer · 2020
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Generalized class incremental learning
Fei Mi, Lingjing Kong, Tao Lin, Kaicheng Yu, and Boi Faltings · 2020
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Self-supervised learning of pretext-invariant representations
Ishan Misra and Laurens van der Maaten · 2020
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itaml: An incremental task-agnostic meta-learning approach
Jathushan Rajasegaran, Salman Khan, Munawar Hayat, Fahad Shahbaz Khan, and Mubarak Shah · 2020
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Are open set classification methods effective on large-scale datasets?
Ryne Roady, Tyler L Hayes, Ronald Kemker, Ayesha Gonzales, and Christopher Kanan · 2020
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Maintaining discrimination and fairness in class incremental learning
Bowen Zhao, Xi Xiao, Guojun Gan, Bin Zhang, and Shutao Xia · 2020
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Open-world semi-supervised learning
Kaidi Cao, Maria Brbic, and Jure Leskovec · 2021
Closest in time.
Self-supervised training enhances online continual learning
Jhair Gallardo, Tyler L. Hayes, and Christopher Kanan · 2021
Closest in time.