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Data pruning, which aims to downsize a large training set into a small informative subset, is crucial for reducing the enormous computational costs of modern deep learning.
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Multi-class Active Learning for Image Classification
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Identity Mappings in Deep Residual Networks
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Deep Residual Learning for Image Recognition
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Deep Learning Scaling is Predictable, Empirically
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Webvision Database: Visual Learning and Understanding from Web Data
Wen Li, Limin Wang, Wei Li, Eirikur Agustsson, and Luc Van Gool · 2017
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Training Deep Neural Networks Using a Noise Adaptation Layer
Jacob Goldberger and Ehud Ben-Reuven · 2017
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Robust Loss Functions under Label Noise for Deep Neural Networks
Aritra Ghosh, Himanshu Kumar, and P Shanti Sastry · 2017
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Inception-v4, inception-resnet and the Impact of Residual Connections on Learning
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An Empirical Study of Example Forgetting during Deep Neural Network Learning
Mariya Toneva, Alessandro Sordoni, Remi Tachet des Combes, Adam Trischler, Yoshua Bengio, and Geoffrey J Gordon · 2018
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Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama · 2018
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Masking: A New Perspective of Noisy Supervision
Bo Han, Jiangchao Yao, Gang Niu, Mingyuan Zhou, Ivor Tsang, Ya Zhang, and Masashi Sugiyama · 2018
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Deep Learning from Noisy Image Labels with Quality Embedding
Jiangchao Yao, Jiajie Wang, Ivor W Tsang, Ya Zhang, Jun Sun, Chengqi Zhang, and Rui Zhang · 2018
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Using Trusted Data to Train Deep Networks on Labels Corrupted by Severe Noise
Dan Hendrycks, Mantas Mazeika, Duncan Wilson, and Kevin Gimpel · 2018
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Mentornet: Learning Data-driven Curriculum for Very Deep Neural Networks on Corrupted Labels
Lu Jiang, Zhengyuan Zhou, Thomas Leung, Li-Jia Li, and Li Fei-Fei · 2018
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Active Learning for Convolutional Neural Networks: A Core-Set Approach
Ozan Sener and Silvio Savarese · 2018
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Selfie: Refurbishing Unclean Samples for Robust Deep Learning
Hwanjun Song, Minseok Kim, and Jae-Gil Lee · 2019
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Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels
Pengfei Chen, Ben Ben Liao, Guangyong Chen, and Shengyu Zhang · 2019
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Selection via Proxy: Efficient Data Selection for Deep Learning
Cody Coleman, Christopher Yeh, Stephen Mussmann, Baharan Mirzasoleiman, Peter Bailis, Percy Liang, Jure Leskovec, and Matei Zaharia · 2019
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Multi-task Curriculum Framework for Open-set Semi-supervised Learning
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Learning Transferable Visual Models from Natural Language Supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Learning with Noisy Labels Revisited: A Study using Real-world Human Annotations
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Deep Learning on a Data Diet: Finding Important Examples Early in Training
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Glister: Generalization based Data Subset Selection for Efficient and Robust Learning
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Neural Architecture Search: A Survey
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Scaling Laws for Neural Language Models
Jared Kaplan, Sam McCandlish, Tom Henighan, Tom B Brown, Benjamin Chess, Rewon Child, Scott Gray, Alec Radford, Jeffrey Wu, and Dario Amodei · 2020
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Language Models are Few-shot Learners
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An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
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Unsupervised Data Augmentation for Consistency Training
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Dividemix: Learning with Noisy Labels as Semi-supervised Learning
Junnan Li, Richard Socher, and Steven CH Hoi · 2020
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Normalized Loss Functions for Deep Learning with Noisy Labels
Xingjun Ma, Hanxun Huang, Yisen Wang, Simone Romano, Sarah Erfani, and James Bailey · 2020
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Robust Curriculum Learning: From Clean Label Detection to Noisy Label Self-correction
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Grad-match: Gradient Matching based Data Subset Selection for Efficient Deep Model Training
Krishnateja Killamsetty, S Durga, Ganesh Ramakrishnan, Abir De, and Rishabh Iyer · 2021
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Consistency Regularization Can Improve Robustness to Label Noise
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Batch Active Learning at Scale
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Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data
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A Continual Learning Survey: Defying Forgetting in Classification Tasks
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Beyond Neural Scaling Laws: Beating Power Law Scaling via Data Pruning
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Learning from Noisy Labels with Deep Neural Networks: A Survey
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Deepcore: A Comprehensive Library for Coreset Selection in Deep Learning
Chengcheng Guo, Bo Zhao, and Yanbing Bai · 2022
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Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning
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Robust Training under Label Noise by Over-parameterization
Sheng Liu, Zhihui Zhu, Qing Qu, and Chong You · 2022
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Active Learning is A Strong Baseline for Data Subset Selection
Dongmin Park, Dimitris Papailiopoulos, and Kangwook Lee · 2022
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Coverage-centric Coreset Selection for High Pruning Rates
Haizhong Zheng, Rui Liu, Fan Lai, and Atul Prakash · 2022
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Moderate Coreset: A Universal Method of Data Selection for Real-world Data-efficient Deep Learning
Xiaobo Xia, Jiale Liu, Jun Yu, Xu Shen, Bo Han, and Tongliang Liu · 2022
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