[18] Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. Deep residual learning for image recognition. In 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016, pages 770-778. IEEE Computer Society, 2016.
[19] Wei-Ning Hsu and Hsuan-Tien Lin. Active learning by learning. In Proceedings of the AAAI Conference on Artificial Intelligence, volume 29, 2015.
[20] Rong Hu, Brian Mac Namee, and Sarah Jane Delany. Off to a good start: Using clustering to select the initial training set in active learning. In Twenty-Third International FLAIRS Conference, 2010.
[21] Achin Jain, Gurumurthy Swaminathan, Paolo Favaro, Hao Yang, Avinash Ravichandran, Hrayr Harutyunyan, Alessandro Achille, Onkar Dabeer, Bernt Schiele, Ashwin Swaminathan, et al. A meta-learning approach to predicting performance and data requirements. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3623-3632, 2023.
[22] Andreas Kirsch, Joost Van Amersfoort, and Yarin Gal. Batchbald: Efficient and diverse batch acquisition for deep bayesian active learning. Advances in neural information processing systems, 32:7026-7037, 2019.
[23] Alex Krizhevsky, Geoffrey Hinton, et al. Learning multiple layers of features from tiny images. Online, 2009.
[24] David D Lewis and William A Gale. A sequential algorithm for training text classifiers. In SIGIR'94, pages 3-12. Springer, 1994.
[25] Rafid Mahmood, Sanja Fidler, and Marc T. Law. Low-budget active learning via wasserstein distance: An integer programming approach. In The Tenth International Conference on Learning Representations, ICLR 2022, Virtual Event, April 25-29, 2022. OpenReview.net, 2022.
[26] Rafid Mahmood, James Lucas, Jose M Alvarez, Sanja Fidler, and Marc Law. Optimizing data collection for machine learning. Advances in Neural Information Processing Systems, 35:29915-29928, 2022.
[27] Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek, and Thomas Brox. Parting with illusions about deep active learning. arXiv preprint arXiv:1912.05361, 2019.
[28] Prateek Munjal, Nasir Hayat, Munawar Hayat, Jamshid Sourati, and Shadab Khan. Towards robust and reproducible active learning using neural networks. In IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2022, New Orleans, LA, USA, June 18-24, 2022, pages 223-232. IEEE, 2022.
[29] Kunkun Pang, Mingzhi Dong, Yang Wu, and Timothy M Hospedales. Dynamic ensemble active learning: A non-stationary bandit with expert advice. In 2018 24th International Conference on Pattern Recognition (ICPR), pages 2269-2276. IEEE, 2018.
[30] Dongmin Park, Dimitris Papailiopoulos, and Kangwook Lee. Active learning is a strong baseline for data subset selection. In Has it Trained Yet? NeurIPS 2022 Workshop, 2022.
[31] Hiranmayi Ranganathan, Hemanth Venkateswara, Shayok Chakraborty, and Sethuraman Panchanathan. Deep active learning for image classification. In 2017 IEEE International Conference on Image Processing (ICIP), pages 3934-3938. IEEE, 2017.
[32] Christopher Schröder and Andreas Niekler. A survey of active learning for text classification using deep neural networks. arXiv preprint arXiv:2008.07267, 2020.
[33] Ozan Sener and Silvio Savarese. Active learning for convolutional neural networks: A core-set approach. In International Conference on Learning Representations, 2018.
[34] Burr Settles. Active learning literature survey. Computer Sciences Technical Report 1648, University of Wisconsin-Madison, 2009.
[35] Changjian Shui, Fan Zhou, Christian Gagné, and Boyu Wang. Deep active learning: Unified and principled method for query and training. In International Conference on Artificial Intelligence and Statistics, pages 1308-1318. PMLR, 2020.