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We introduce a Parametric Information Maximization (PIM) model for the Generalized Category Discovery (GCD) problem.
Classification and analysis of multivariate observations
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Imagenet: A large-scale hierarchical image database
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Learning multiple layers of features from tiny images
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Noise-contrastive estimation: A new estimation principle for unnormalized statistical models
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Discriminative clustering by regularized information maximization
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The caltech-ucsd birds-200-2011 dataset
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3d object representations for fine-grained categorization
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Adam: A method for stochastic optimization
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Towards open set deep networks
Abhijit Bendale and Terrance E Boult · 2016
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Deep residual learning for image recognition
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Learning discrete representations via information maximizing self-augmented training
Weihua Hu, Takeru Miyato, Seiya Tokui, Eiichi Matsumoto, and Masashi Sugiyama · 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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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Learning deep representations by mutual information estimation and maximization
R Devon Hjelm, Alex Fedorov, Samuel Lavoie-Marchildon, Karan Grewal, Phil Bachman, Adam Trischler, and Yoshua Bengio · 2018
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Learning representations by maximizing mutual information across views
Philip Bachman, R Devon Hjelm, and William Buchwalter · 2019
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Mixmatch: A holistic approach to semi-supervised learning
David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Oliver, and Colin A Raffel · 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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The herbarium challenge 2019 dataset
Kiat Chuan Tan, Yulong Liu, Barbara Ambrose, Melissa Tulig, and Serge Belongie · 2019
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On mutual information maximization for representation learning
Michael Tschannen, Josip Djolonga, Paul K Rubenstein, Sylvain Gelly, and Mario Lucic · 2019
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S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
Emerging properties in self-supervised vision transformers
Mathilde Caron, Hugo Touvron, Ishan Misra, Hervé Jégou, Julien Mairal, Piotr Bojanowski, and Armand Joulin · 2021
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An image is worth 16x16 words: Transformers for image recognition at scale
Alexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn, Xiaohua Zhai, Thomas Unterthiner, Mostafa Dehghani, Matthias Minderer, Georg Heigold, Sylvain Gelly, Jakob Uszkoreit, and Neil Houlsby · 2021
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A unified objective for novel class discovery
Enrico Fini, Enver Sangineto, Stéphane Lathuilière, Zhun Zhong, Moin Nabi, and Elisa Ricci · 2021
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Autonovel: Automatically discovering and learning novel visual categories
Kai Han, Sylvestre-Alvise Rebuffi, Sebastien Ehrhardt, Andrea Vedaldi, and Andrew Zisserman · 2021
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Deep clustering: On the link between discriminative models and k-means
Mohammed Jabi, Marco Pedersoli, Amar Mitiche, and Ismail Ben Ayed · 2021
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A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, and Ismail Ben Ayed · 2020
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Information maximization for few-shot learning
Malik Boudiaf, Imtiaz Ziko, Jérôme Rony, José Dolz, Pablo Piantanida, and Ismail Ben Ayed · 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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Rankmi: A mutual information maximizing ranking loss
Mete Kemertas, Leila Pishdad, Konstantinos G Derpanis, and Afsaneh Fazly · 2020
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Supervised contrastive learning
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Exploring category-agnostic clusters for open-set domain adaptation
Yingwei Pan, Ting Yao, Yehao Li, Chong-Wah Ngo, and Tao Mei · 2020
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Joint representation learning and novel category discovery on single-and multi-modal data
Xuhui Jia, Kai Han, Yukun Zhu, and Bradley Green · 2021
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Entropic out-of-distribution detection
David Macêdo, Tsang Ing Ren, Cleber Zanchettin, Adriano L. I. Oliveira, and Teresa Ludermir · 2021
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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 · 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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Neighborhood contrastive learning for novel class discovery
Zhun Zhong, Enrico Fini, Subhankar Roy, Zhiming Luo, Elisa Ricci, and Nicu Sebe · 2021
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Openmix: Reviving known knowledge for discovering novel visual categories in an open world
Zhun Zhong, Linchao Zhu, Zhiming Luo, Shaozi Li, Yi Yang, and Nicu Sebe · 2021
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Open-world semi-supervised learning
Kaidi Cao, Maria Brbic, and Jure Leskovec · 2022
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Generalized category discovery
Sagar Vaze, Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2022
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Mutual information-guided knowledge transfer for novel class discovery
Chuyu Zhang, Chuanyang Hu, Ruijie Xu, Zhitong Gao, Qian He, and Xuming He · 2022
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Simmatch: Semi-supervised learning with similarity matching
Mingkai Zheng, Shan You, Lang Huang, Fei Wang, Chen Qian, and Chang Xu · 2022
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