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Active learning promises to improve annotation efficiency by iteratively selecting the most important data to be annotated first.
Margin based active learning
Maria-Florina Balcan, Andrei Broder, and Tong Zhang · 2007
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Entropy-based active learning for object recognition
Alex Holub, Pietro Perona, and Michael C Burl · 2008
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Curriculum learning
Yoshua Bengio, Jérôme Louradour, Ronan Collobert, and Jason Weston · 2009
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Facility location: concepts, models, algorithms and case studies
Reza Zanjirani Farahani and Masoud Hekmatfar · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Active learning literature survey
Burr Settles · 2009
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Active batch learning with stochastic query by forest
Alexander Borisov, Eugene Tuv, and George Runger · 2010
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Solving the cold-start problem in recommender systems with social tags
Zi-Ke Zhang, Chuang Liu, Yi-Cheng Zhang, and Tao Zhou · 2010
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An item-oriented recommendation algorithm on cold-start problem
Tian Qiu, Guang Chen, Zi-Ke Zhang, and Tao Zhou · 2011
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A collaborative filtering approach to mitigate the new user cold start problem
Jesús Bobadilla, Fernando Ortega, Antonio Hernando, and Jesús Bernal · 2012
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Cold-start active learning with robust ordinal matrix factorization
Neil Houlsby, José Miguel Hernández-Lobato, and Zoubin Ghahramani · 2014
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A new active labeling method for deep learning
Dan Wang and Yi Shang · 2014
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Active bias: Training more accurate neural networks by emphasizing high variance samples
Haw-Shiuan Chang, Erik Learned-Miller, and Andrew McCallum · 2017
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Deep bayesian active learning with image data
Yarin Gal, Riashat Islam, and Zoubin Ghahramani · 2017
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A survey on deep learning in medical image analysis
Geert Litjens, Thijs Kooi, Babak Ehteshami Bejnordi, Arnaud Arindra Adiyoso Setio, Francesco Ciompi, Mohsen Ghafoorian, Jeroen Awm Van Der Laak, Bram Van Ginneken, and Clara I Sánchez · 2017
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Active learning for convolutional neural networks: A core-set approach
Ozan Sener and Silvio Savarese · 2017
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Fine-tuning convolutional neural networks for biomedical image analysis: actively and incrementally
Zongwei Zhou, Jae Shin, Lei Zhang, Suryakanth Gurudu, Michael Gotway, and Jianming Liang · 2017
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Umap: Uniform manifold approximation and projection for dimension reduction
Leland McInnes, John Healy, and James Melville · 2018
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Multigrain: a unified image embedding for classes and instances
Maxim Berman, Hervé Jégou, Andrea Vedaldi, Iasonas Kokkinos, and Matthijs Douze · 2019
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The liver tumor segmentation benchmark (lits)
Patrick Bilic, Patrick Ferdinand Christ, Eugene Vorontsov, Grzegorz Chlebus, Hao Chen, Qi Dou, Chi-Wing Fu, Xiao Han, Pheng-Ann Heng, Jürgen Hesser, et al · 2019
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Scaling and benchmarking self-supervised visual representation learning
Priya Goyal, Dhruv Mahajan, Abhinav Gupta, and Ishan Misra · 2019
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Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study
Jakob Nikolas Kather, Johannes Krisam, Pornpimol Charoentong, Tom Luedde, Esther Herpel, Cleo-Aron Weis, Timo Gaiser, Alexander Marx, Nektarios A. Valous, Dyke Ferber, Lina Jansen, Constantino Carlos Reyes-Aldasoro, Inka Zörnig, Dirk Jäger, Hermann Brenner, Jenny Chang-Claude, Michael Hoffmeister, and Niels Halama · 2019
Cited alongside, same era.
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal · 2019
Cited alongside, same era.
Parting with illusions about deep active learning
Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek, and Thomas Brox · 2019
Cited alongside, same era.
Variational adversarial active learning
Samarth Sinha, Sayna Ebrahimi, and Trevor Darrell · 2019
Cited alongside, same era.
Intelligent labeling based on fisher information for medical image segmentation using deep learning
Jamshid Sourati, Ali Gholipour, Jennifer G Dy, Xavier Tomas-Fernandez, Sila Kurugol, and Simon K Warfield · 2019
Reducing label effort: Self-supervised meets active learning
Javad Zolfaghari Bengar, Joost van de Weijer, Bartlomiej Twardowski, and Bogdan Raducanu · 2021
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On initial pools for deep active learning
Akshay L Chandra, Sai Vikas Desai, Chaitanya Devaguptapu, and Vineeth N Balasubramanian · 2021
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Semi-supervised active learning with temporal output discrepancy
Siyu Huang, Tianyang Wang, Haoyi Xiong, Jun Huan, and Dejing Dou · 2021
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Covid-19 detection from scarce chest x-ray image data using few-shot deep learning approach
Shruti Jadon · 2021
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Siddharth Karamcheti, Ranjay Krishna, Li Fei-Fei, and Christopher D Manning · 2021
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Cited alongside, same era.
Integrating active learning and transfer learning for carotid intima-media thickness video interpretation
Zongwei Zhou, Jae Shin, Ruibin Feng, R Todd Hurst, Christopher B Kendall, and Jianming Liang · 2019
Cited alongside, same era.
Addressing the item cold-start problem by attribute-driven active learning
Yu Zhu, Jinghao Lin, Shibi He, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai · 2019
Cited alongside, same era.
A dataset of microscopic peripheral blood cell images for development of automatic recognition systems
Andrea Acevedo, Anna Merino, Santiago Alférez, Ángel Molina, Laura Boldú, and José Rodellar · 2020
Cited alongside, same era.
Contextual diversity for active learning
Sharat Agarwal, Himanshu Arora, Saket Anand, and Chetan Arora · 2020
Cited alongside, same era.
A simple framework for contrastive learning of visual representations
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton · 2020
Cited alongside, same era.
Improved baselines with momentum contrastive learning
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2020
Cited alongside, same era.
Consistency-based semi-supervised active learning: Towards minimizing labeling cost
Mingfei Gao, Zizhao Zhang, Guo Yu, Sercan Ö Arık, Larry S Davis, and Tomas Pfister · 2020
Cited alongside, same era.
Best practices in pool-based active learning for image classification
Adrian Lang, Christoph Mayer, and Radu Timofte · 2021
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Contrastive clustering
Yunfan Li, Peng Hu, Zitao Liu, Dezhong Peng, Joey Tianyi Zhou, and Xi Peng · 2021
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Bayesian active learning with pretrained language models
Katerina Margatina, Loic Barrault, and Nikolaos Aletras · 2021
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A simple baseline for low-budget active learning
Kossar Pourahmadi, Parsa Nooralinejad, and Hamed Pirsiavash · 2021
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Rethinking deep active learning: Using unlabeled data at model training
Oriane Siméoni, Mateusz Budnik, Yannis Avrithis, and Guillaume Gravier · 2021
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Training data-efficient image transformers & distillation through attention
Hugo Touvron, Matthieu Cord, Matthijs Douze, Francisco Massa, Alexandre Sablayrolles, and Herv’e J’egou · 2021
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Medmnist v2: A large-scale lightweight benchmark for 2d and 3d biomedical image classification
Jiancheng Yang, Rui Shi, Donglai Wei, Zequan Liu, Lin Zhao, Bilian Ke, Hanspeter Pfister, and Bingbing Ni · 2021
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A review of deep learning in medical imaging: Imaging traits, technology trends, case studies with progress highlights, and future promises
S Kevin Zhou, Hayit Greenspan, Christos Davatzikos, James S Duncan, Bram van Ginneken, Anant Madabhushi, Jerry L Prince, Daniel Rueckert, and Ronald M Summers · 2021
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Towards Annotation-Efficient Deep Learning for Computer-Aided Diagnosis
Zongwei Zhou · 2021
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Active, continual fine tuning of convolutional neural networks for reducing annotation efforts
Zongwei Zhou, Jae Y Shin, Suryakanth R Gurudu, Michael B Gotway, and Jianming Liang · 2021
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Moco demo: Cifar-10
Xinlei Chen, Haoqi Fan, Ross Girshick, and Kaiming He · 2022
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Active learning on a budget: Opposite strategies suit high and low budgets
Guy Hacohen, Avihu Dekel, and Daphna Weinshall · 2022
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Warm start active learning with proxy labels and selection via semi-supervised fine-tuning
Vishwesh Nath, Dong Yang, Holger R Roth, and Daguang Xu · 2022
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Beyond neural scaling laws: beating power law scaling via data pruning
Ben Sorscher, Robert Geirhos, Shashank Shekhar, Surya Ganguli, and Ari S Morcos · 2022
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Videomae: Masked autoencoders are data-efficient learners for self-supervised video pre-training
Zhan Tong, Yibing Song, Jue Wang, and Limin Wang · 2022
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Active learning through a covering lens
Ofer Yehuda, Avihu Dekel, Guy Hacohen, and Daphna Weinshall · 2022
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