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Active Learning (AL) aims to enhance the performance of deep models by selecting the most informative samples for annotation from a pool of unlabeled data.
Deep batch active learning by diverse, uncertain gradient lower bounds
Ash, J. T.; Zhang, C.; Krishnamurthy, A.; Langford, J.; and Agarwal, A. 2019 · 1906
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Submodularity in data subset selection and active learning
Wei, K.; Iyer, R.; and Bilmes, J. 2015 · 1963
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Some methods for classification and analysis of multivariate observations
MacQueen, J.; et al. 1967 · 1967
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Query by committee
Seung, H. S.; Opper, M.; and Sompolinsky, H. 1992 · 1992
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Representative sampling for text classification using support vector machines
Xu, Z.; Yu, K.; Tresp, V.; Xu, X.; and Wang, J. 2003 · 2003
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Active learning using pre-clustering
Nguyen, H. T.; and Smeulders, A. 2004 · 2004
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Margin based active learning
Balcan, M.-F.; Broder, A.; and Zhang, T. 2007 · 2007
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Learning multiple layers of features from tiny images
Krizhevsky, A.; Hinton, G.; et al. 2009 · 2009
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Toward open set recognition
Scheirer, W. J.; de Rezende Rocha, A.; Sapkota, A.; and Boult, T. E. 2012 · 2012
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Latent Structured Active Learning
Luo, W.; Schwing, A.; and Urtasun, R. 2013 · 2013
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Generative adversarial nets
Goodfellow, I.; Pouget-Abadie, J.; Mirza, M.; Xu, B.; Warde-Farley, D.; Ozair, S.; Courville, A.; and Bengio, Y. 2014 · 2014
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A new active labeling method for deep learning
Wang, D.; and Shang, Y. 2014 · 2014
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Faster r-cnn: Towards real-time object detection with region proposal networks
Ren, S.; He, K.; Girshick, R.; and Sun, J. 2015 · 2015
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Tiny imagenet classification with convolutional neural networks
Yao, L.; and Miller, J. 2015 · 2015
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Towards open set deep networks
Bendale, A.; and Boult, T. E. 2016 · 2016
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Deep residual learning for image recognition
He, K.; Zhang, X.; Ren, S.; and Sun, J. 2016 · 2016
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You only look once: Unified, real-time object detection
Redmon, J.; Divvala, S.; Girshick, R.; and Farhadi, A. 2016 · 2016
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An overview of gradient descent optimization algorithms
Ruder, S. 2016 · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
Sohn, K. 2016 · 2016
Cited alongside, same era.
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs
Chen, L.-C.; Papandreou, G.; Kokkinos, I.; Murphy, K.; and Yuille, A. L. 2017 · 2017
Cited alongside, same era.
Generative openmax for multi-class open set classification
Ge, Z.; Demyanov, S.; Chen, Z.; and Garnavi, R. 2017 · 2017
Cited alongside, same era.
Active learning for convolutional neural networks: A core-set approach
Sener, O.; and Savarese, S. 2017 · 2017
Cited alongside, same era.
Similar: Submodular information measures based active learning in realistic scenarios
Kothawade, S.; Beck, N.; Killamsetty, K.; and Iyer, R. 2021 · 2021
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Class anchor clustering: A loss for distance-based open set recognition
Miller, D.; Sunderhauf, N.; Milford, M.; and Dayoub, F. 2021 · 2021
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Learning transferable visual models from natural language supervision
Radford, A.; Kim, J. W.; Hallacy, C.; Ramesh, A.; Goh, G.; Agarwal, S.; Sastry, G.; Askell, A.; Mishkin, P.; Clark, J.; et al. 2021 · 2021
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Ovanet: One-vs-all network for universal domain adaptation
Saito, K.; and Saenko, K. 2021 · 2021
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Active learning by query by committee with robust divergences
Hino, H.; and Eguchi, S. 2022 · 2022
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Pmal: Open set recognition via robust prototype mining
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Open Set Learning with Counterfactual Images
Neal, L.; Olson, M.; Fern, X.; Wong, W.-K.; and Li, F. 2018 · 2018
Cited alongside, same era.
Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning
Kirsch, A.; Van Amersfoort, J.; and Gal, Y. 2019 · 2019
Cited alongside, same era.
C2ae: Class conditioned auto-encoder for open-set recognition
Oza, P.; and Patel, V. M. 2019 · 2019
Cited alongside, same era.
Pytorch: An imperative style, high-performance deep learning library
Paszke, A.; Gross, S.; Massa, F.; Lerer, A.; Bradbury, J.; Chanan, G.; Killeen, T.; Lin, Z.; Gimelshein, N.; Antiga, L.; et al. 2019 · 2019
Cited alongside, same era.
Efficient parameter-free clustering using first neighbor relations
Sarfraz, S.; Sharma, V.; and Stiefelhagen, R. 2019 · 2019
Cited alongside, same era.
Bayesian generative active deep learning
Tran, T.; Do, T.-T.; Reid, I.; and Carneiro, G. 2019 · 2019
Cited alongside, same era.
Classification-reconstruction learning for open-set recognition
Yoshihashi, R.; Shao, W.; Kawakami, R.; You, S.; Iida, M.; and Naemura, T. 2019 · 2019
Cited alongside, same era.
Lu, J.; Xu, Y.; Li, H.; Cheng, Z.; and Niu, Y. 2022 · 2022
Later among the works it cites.
Difficulty-aware simulator for open set recognition
Moon, W.; Park, J.; Seong, H. S.; Cho, C.-H.; and Heo, J.-P. 2022 · 2022
Later among the works it cites.
Active learning for open-set annotation
Ning, K.-P.; Zhao, X.; Li, Y.; and Huang, S.-J. 2022 · 2022
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Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
Park, D.; Shin, Y.; Bang, J.; Lee, Y.; Song, H.; and Lee, J.-G. 2022 · 2022
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Active learning by feature mixing
Parvaneh, A.; Abbasnejad, E.; Teney, D.; Haffari, G. R.; Van Den Hengel, A.; and Shi, J. Q. 2022 · 2022
Later among the works it cites.
Mixture of Teacher Experts for Source-Free Domain Adaptive Object Detection
Vs, V.; Oza, P.; Sindagi, V. A.; and Patel, V. M. 2022 · 2022
Later among the works it cites.
Unified Classification and Rejection: A One-versus-All Framework
Cheng, Z.; Zhang, X.-Y.; and Liu, C.-L. 2023 · 2023
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Kirillov, A.; Mintun, E.; Ravi, N.; Mao, H.; Rolland, C.; Gustafson, L.; Xiao, T.; Whitehead, S.; Berg, A. C.; Lo, W.-Y.; et al. 2023 · 2023
Closest in time.
Open-Set Automatic Target Recognition
Safaei, B.; Vibashan, V.; de Melo, C. M.; Hu, S.; and Patel, V. M. 2023 · 2023
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Towards online domain adaptive object detection
VS, V.; Oza, P.; and Patel, V. M. 2023 · 2023
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Mask-free OVIS: Open-Vocabulary Instance Segmentation without Manual Mask Annotations
VS, V.; Yu, N.; Xing, C.; Qin, C.; Gao, M.; Niebles, J. C.; Patel, V. M.; and Xu, R. 2023 · 2023
Closest in time.