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We explore the problem of Incremental Generalized Category Discovery (IGCD).
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3d object representations for fine-grained categorization
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Abhijit Bendale and Terrance E Boult · 2015
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Human-level concept learning through probabilistic program induction
Brenden M Lake, Ruslan Salakhutdinov, and Joshua B Tenenbaum · 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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Abhijit Bendale and Terrance E Boult · 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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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
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Temporal ensembling for semi-supervised learning
Samuli Laine and Timo Aila · 2017
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Sgdr: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2017
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icarl: Incremental classifier and representation learning
Sylvestre-Alvise Rebuffi, Alexander Kolesnikov, Georg Sperl, and Christoph H Lampert · 2017
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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Learning to cluster in order to transfer across domains and tasks
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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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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin Raffel · 2019
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Learning and the unknown: Surveying steps toward open world recognition
Terrance E Boult, Steve Cruz, Akshay Raj Dhamija, M Gunther, James Henrydoss, and Walter J Scheirer · 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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Multi-class classification without multi-class labels
Benchmarking representation learning for natural world image collections
Grant Van Horn, Elijah Cole, Sara Beery, Kimberly Wilber, Serge Belongie, and Oisin Mac Aodha · 2021
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Novel visual category discovery with dual ranking statistics and mutual knowledge distillation
Bingchen Zhao and Kai Han · 2021
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Masked siamese networks for label-efficient learning
Mahmoud Assran, Mathilde Caron, Ishan Misra, Piotr Bojanowski, Florian Bordes, Pascal Vincent, Armand Joulin, Mike Rabbat, and Nicolas Ballas · 2022
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Open-world semi-supervised learning
Kaidi Cao, Maria Brbić, and Jure Leskovec · 2022
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Parametric information maximization for generalized category discovery
Florent Chiaroni, Jose Dolz, Ziko Imtiaz Masud, Amar Mitiche, and Ismail Ben Ayed · 2022
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Yen-Chang Hsu, Zhaoyang Lv, Joel Schlosser, Phillip Odom, and Zsolt Kira · 2019
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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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 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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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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Momentum contrast for unsupervised visual representation learning
Kaiming He, Haoqi Fan, Yuxin Wu, Saining Xie, and Ross Girshick · 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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Xcon: Learning with experts for fine-grained category discovery
Yixin Fei, Zhongkai Zhao, Siwei Yang, and Bingchen Zhao · 2022
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Novel class discovery without forgetting
KJ Joseph, Sujoy Paul, Gaurav Aggarwal, Soma Biswas, Piyush Rai, Kai Han, and Vineeth N Balasubramanian · 2022
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Continual learning with evolving class ontologies
Zhiqiu Lin, Deepak Pathak, Yu-Xiong Wang, Deva Ramanan, and Shu Kong · 2022
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Residual tuning: Toward novel category discovery without labels
Yu Liu and Tinne Tuytelaars · 2022
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Unified probabilistic deep continual learning through generative replay and open set recognition
Martin Mundt, Iuliia Pliushch, Sagnik Majumder, Yongwon Hong, and Visvanathan Ramesh · 2022
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Class-incremental novel class discovery
Subhankar Roy, Mingxuan Liu, Zhun Zhong, Nicu Sebe, and Elisa Ricci · 2022
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Generalized category discovery
Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2022
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Open-set recognition: a good closed-set classifier is all you need?
Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2022
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Grow and merge: A unified framework for continuous categories discovery
Xinwei Zhang, Jianwen Jiang, Yutong Feng, Zhi-Fan Wu, Xibin Zhao, Hai Wan, Mingqian Tang, Rong Jin, and Yue Gao · 2022
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Taxonomic class incremental learning
Yuzhao Chen, Zonghuan Li, Zhiyuan Hu, and Nuno Vasconcelos · 2023
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iNaturalist
iNaturalist · 2023
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Geonet: Benchmarking unsupervised adaptation across geographies
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Large-scale pre-trained models are surprisingly strong in incremental novel class discovery
Mingxuan Liu, Subhankar Roy, Zhun Zhong, Nicu Sebe, and Elisa Ricci · 2023
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A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning
Martin Mundt, Yongwon Hong, Iuliia Pliushch, and Visvanathan Ramesh · 2023
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Novel class discovery: an introduction and key concepts
Colin Troisemaine, Vincent Lemaire, Stéphane Gosselin, Alexandre Reiffers-Masson, Joachim Flocon-Cholet, and Sandrine Vaton · 2023
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Parametric classification for generalized category discovery: A baseline study
Xin Wen, Bingchen Zhao, and Xiaojuan Qi · 2023
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Deep class-incremental learning: A survey
Da-Wei Zhou, Qi-Wei Wang, Zhi-Hong Qi, Han-Jia Ye, De-Chuan Zhan, and Ziwei Liu · 2023
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