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Generalized Category Discovery (GCD) aims to discover novel categories in unlabelled datasets using knowledge learned from labelled samples.
Some methods for classification and analysis of multivariate observations
James MacQueen · 1967
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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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Learning multiple layers of features from tiny images
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Caltech-ucsd birds 200
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3d object representations for fine-grained categorization
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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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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
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Normface: L2 hypersphere embedding for face verification
Feng Wang, Xiang Xiang, Jian Cheng, and Alan Loddon Yuille · 2017
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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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Unsupervised representation learning by predicting image rotations
Spyros Gidaris, Praveer Singh, and Nikos Komodakis · 2018
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Realistic evaluation of deep semi-supervised learning algorithms
Avital Oliver, Augustus Odena, Colin Raffel, Ekin D Cubuk, and Ian J 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 to discover novel visual categories via deep transfer clustering
Kai Han, Andrea Vedaldi, and Andrew Zisserman · 2019
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Billion-scale similarity search with GPUs
Jeff Johnson, Matthijs Douze, and Hervé Jégou · 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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S4l: Self-supervised semi-supervised learning
Xiaohua Zhai, Avital Oliver, Alexander Kolesnikov, and Lucas Beyer · 2019
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Self-labelling via simultaneous clustering and representation learning
Yuki M. Asano, Christian Rupprecht, and Andrea Vedaldi · 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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A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
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Semi-supervised learning under class distribution mismatch
Yanbei Chen, Xiatian Zhu, Wei Li, and Shaogang Gong · 2020
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Bootstrap your own latent-a new approach to self-supervised learning
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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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Long-tail learning via logit adjustment
Aditya Krishna Menon, Sadeep Jayasumana, Ankit Singh Rawat, Himanshu Jain, Andreas Veit, and Sanjiv Kumar · 2021
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Openmatch: Open-set semi-supervised learning with open-set consistency regularization
Kuniaki Saito, Donghyun Kim, and Kate Saenko · 2021
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Long-tailed recognition by routing diverse distribution-aware experts
Xudong Wang, Long Lian, Zhongqi Miao, Ziwei Liu, and Stella X Yu · 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
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Lan-Zhe Guo, Zhen-Yu Zhang, Yuan Jiang, Yu-Feng Li, and Zhi-Hua Zhou · 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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Semi-supervised learning with scarce annotations
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Balanced meta-softmax for long-tailed visual recognition
Jiawei Ren, Cunjun Yu, Xiao Ma, Haiyu Zhao, Shuai Yi, and Hongsheng Li · 2020
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Fixmatch: Simplifying semi-supervised learning with consistency and confidence
Kihyuk Sohn, David Berthelot, Chun-Liang Li, Zizhao Zhang, Nicholas Carlini, Ekin D Cubuk, Alex Kurakin, Han Zhang, and Colin Raffel · 2020
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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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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
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Discriminability-transferability trade-off: An information-theoretic perspective
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Xcon: Learning with experts for fine-grained category discovery
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Class-incremental novel class discovery
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Generalized category discovery
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Debiased learning from naturally imbalanced pseudo-labels
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Self-supervised visual representation learning with semantic grouping
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Opencon: Open-world contrastive learning
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Incremental generalized category discovery
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Learning semi-supervised gaussian mixture models for generalized category discovery
Bingchen Zhao, Xin Wen, and Kai Han · 2023
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