Deep Bayesian Active Learning with Image Data

Cited by (392)

Items 101–200 of 392

1. Investigating Active Learning and Meta-Learning for Iterative Peptide Design
   Rainier Barrett, Andrew D. White · 2020 · Journal of Chemical Information and Modeling
2. Adaptive Self-training for Few-shot Neural Sequence Labeling
   Yaqing Wang, Subhabrata Mukherjee, Haoda Chu, Yuancheng Tu, Ming Wu, Jing Gao, Ahmed Hassan Awadallah · 2020 · arXiv (Cornell University)
3. How Certain is Your Transformer?
   Artem Shelmanov, Evgenii Tsymbalov, Dmitri Puzyrev, Kirill Fedyanin, Alexander Panchenko, Maxim Panov · 2021 · Conference of the European Chapter of the Association for Computational Linguistics
4. SPINBIS: Spintronics-Based Bayesian Inference System With Stochastic Computing
   Xiaotao Jia, Jianlei Yang, Pengcheng Dai, Runze Liu, Yiran Chen, Weisheng S. Zhao · 2019 · IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
5. Importance of Self-Consistency in Active Learning for Semantic Segmentation
   S. Alireza Golestaneh, Kris Kitani · 2020 · British Machine Vision Conference (BMVC)
6. Contrastive Coding for Active Learning under Class Distribution Mismatch
   Pan Du, Suyun Zhao, Hui Chen, Shuwen Chai, Hong Chen, Cuiping Li · 2021 · IEEE/CVF International Conference on Computer Vision (ICCV)
7. Deep orientation: Fast and Robust Upper Body orientation Estimation for Mobile Robotic Applications
   Benjamin Lewandowski, Daniel Seichter, Tim Wengefeld, Lennard Pfennig, Helge Drumm, Horst–Michael Groß · 2019 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
8. How useful is active learning for image‐based plant phenotyping?
   Koushik Nagasubramanian, T. Jubery, Fateme Fotouhi Ardakani, S. Mirnezami, Asheesh K. Singh, Arti Singh, S. Sarkar, B. Ganapathysubramanian · 2020 · The Plant Phenome Journal
9. Multi-Fingered Active Grasp Learning
   Qingkai Lu, Mark Van der Merwe, Tucker Hermans · 2020 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
10. Advanced Active Learning Strategies for Object Detection
    Schmidt, Sebastian, Rao, Qing, Tatsch, Julian, Knoll, Alois · 2020 · 2020 IEEE Intelligent Vehicles Symposium (IV)
11. On the Practicality of Deterministic Epistemic Uncertainty
    Janis Postels, Mattia Segù, Tao Sun, Sieber, Luca Daniel, Luc Van Gool, Fisher Yu, Federico Tombari · 2021 · Repository for Publications and Research Data (ETH Zurich)
12. Uncertainty Based Detection and Relabeling of Noisy Image Labels
    Jan M. Köhler, Maximilian Autenrieth, William Beluch · 2019 · arXiv (Cornell University)
13. Scalpel-CD: Leveraging Crowdsourcing and Deep Probabilistic Modeling for Debugging Noisy Training Data
    Jie Yang, Alisa Smirnova, Dingqi Yang, Gianluca Demartini, Yuan Lu, Philippe Cudré-Mauroux · 2019 · International World Wide Web Conference (WWW)
14. Impact of Base Dataset Design on Few-Shot Image Classification
    Othman Sbai, Camille Couprie, Mathieu Aubry · 2020 · Lecture notes in computer science
15. Expressive Priors in Bayesian Neural Networks: Kernel Combinations and Periodic Functions
    Pearce, Tim, Russell Tsuchida, Mohamed Zaki, Alexandra Melike Brintrup, Andy D. Neely · 2019 · arXiv (Cornell University)
16. Parting with Illusions about Deep Active Learning
    Sudhanshu Mittal, Maxim Tatarchenko, Özgün Çiçek, T. Brox · 2019 · arXiv.org
17. Training-Free Uncertainty Estimation for Dense Regression: Sensitivity as a Surrogate
    Mi Lu, Hao Wang, Yonglong Tian, Hao He, Nir Shavit · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
18. Rethinking Anticipation Tasks: Uncertainty-Aware Anticipation of Sparse Surgical Instrument Usage for Context-Aware Assistance
    Dominik Rivoir, Sebastian Bodenstedt, Isabel Funke, Felix von Bechtolsheim, Marius Distler, Jürgen Weitz, Stefanie Speidel · 2020 · Lecture notes in computer science
19. Similarity Search for Efficient Active Learning and Search of Rare Concepts
    Cody Coleman, Edward Chou, Julian Katz-Samuels, Sean Chang Culatana, Peter D. Bailis, Alexander C. Berg, Robert D. Nowak, Roshan Sumbaly, Matei Zaharia, İsmet Zeki Yalnız · 2022 · Proceedings of the AAAI Conference on Artificial Intelligence
20. Dual Adversarial Network for Deep Active Learning
    Shuo Wang, Yuexiang Li, Kai Ma, Ruhui Ma, Haibing Guan, Yefeng Zheng · 2020 · Lecture notes in computer science
21. Subsequence Based Deep Active Learning for Named Entity Recognition
    Puria Radmard, Yassir Fathullah, Aldo Lipani · 2021
22. Towards An Active Learning Approach To Tool Condition Monitoring With Bayesian Deep Learning
    Giovanna Martínez-Arellano, Svetan M. Ratchev · 2019 · European Conference on Modelling and Simulation (ECMS)
23. Deep Active Learning in Remote Sensing for data efficient Change Detection
    Vít Růžička, Stefano D’Aronco, Jan Dirk Wegner, Konrad Schindler · 2020 · arXiv (Cornell University)
24. Multi-class Text Classification using BERT-based Active Learning
    Sumanth Prabhu, Moosa Mohamed, Hemant Misra · 2021 · arXiv (Cornell University)
25. Gone Fishing: Neural Active Learning with Fisher Embeddings
    Jordan T. Ash, Surbhi Goel, Akshay Krishnamurthy, Sham M. Kakade · 2021 · arXiv (Cornell University)
26. LETS: A Label-Efficient Training Scheme for Aspect-Based Sentiment Analysis by Using a Pre-Trained Language Model
    Heereen Shim, Dietwig Lowet, Stijn Luca, Bart Vanrumste · 2021 · IEEE Access
27. ActiveEA: Active Learning for Neural Entity Alignment
    Bing Liu, Harrisen Scells, Guido Zuccon, Wen Hua, Genghong Zhao · 2021 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
28. Deep Active Learning for Joint Classification & Segmentation with Weak Annotator
    Soufiane Belharbi, Ismail Ben Ayed, Luke Martin McCaffrey, Éric Granger · 2021 · IEEE Winter Conference on Applications of Computer Vision
29. DEAL: Deep Evidential Active Learning for Image Classification
    Patrick Hemmer, Niklas Kühl, Jakob Schöffer · 2021 · Advances in intelligent systems and computing
30. Active Learning with Deep Pre-trained Models for Sequence Tagging of Clinical and Biomedical Texts
    Artem Shelmanov, Vadim Liventsev, Danil Kireev, Nikita Khromov, Alexander Panchenko, Irina Fedulova, Dmitry V. Dylov · 2019 · IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
31. Temporal Coherence for Active Learning in Videos
    Zolfaghari Bengar, Javad, Gonzalez-Garcia, Abel, Villalonga, Gabriel, Raducanu, Bogdan, Habibi Aghdam, Hamed, Mozerov, Mikhail, Lopez, Antonio M., van de Weijer, Joost · 2019 · 2019 IEEE/CVF International Conference on Computer Vision Workshop (ICCVW)
32. An active learning method for diabetic retinopathy classification with uncertainty quantification
    Muhammad Ahtazaz Ahsan, Adnan Qayyum, Adeel Razi, Junaid Qadir · 2022 · Medical & Biological Engineering & Computing
33. Unsupervised Active Learning via Subspace Learning
    Changsheng Li, Kaihang Mao, Lingyan Liang, Dongchun Ren, Wei Zhang, Ye Yuan, Guoren Wang · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
34. PAC Confidence Sets for Deep Neural Networks via Calibrated Prediction
    Sangdon Park, Osbert Bastani, Nikolai Matni, Insup Lee · 2019 · arXiv (Cornell University)
35. Multimodal Surprise Adequacy Analysis of Inputs for Natural Language Processing DNN Models
    Seah Kim, Shin Yoo · 2021 · International Conference/Workshop on Automation of Software Test
36. Active and Incremental Learning with Weak Supervision
    Clemens-Alexander Brust, Christoph Käding, Joachim Denzler · 2020 · Künstliche Intell.
37. Efficient Conditional Pre-training for Transfer Learning
    Shuvam Chakraborty, Burak Uzkent, Kumar Ayush, Kumar Tanmay, Evan Sheehan, Stefano Ermon · 2022 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
38. Active Learning for Bayesian 3D Hand Pose Estimation
    Razvan Caramalau, Binod Bhattarai, Tae‐Kyun Kim · 2021 · IEEE Winter Conference on Applications of Computer Vision
39. LTP: A New Active Learning Strategy for Bert-CRF Based Named Entity Recognition
    Mingyi Liu, Zhiying Tu, Zhongjie Wang, Xiaofei Xu · 2020 · arXiv (Cornell University)
40. Reliable Label-Efficient Learning for Biomedical Image Recognition
    Yun Gu, Mali Shen, Jie Yang, Guang‐Zhong Yang · 2018 · IEEE Transactions on Biomedical Engineering
41. Distributed Active Learning Strategies on Edge Computing
    Jia Qian, Sarada Prasad Gochhayat, Lars Kai Hansen · 2019 · IEEE International Conference on Cyber Security and Cloud Computing / IEEE International Conference on Edge Computing and Scalable Cloud (CSCloud/EdgeCom)
42. Learning to Rank for Active Learning: A Listwise Approach
    Minghan Li, Xia-Lei Liu, J. van de Weijer, B. Raducanu · 2020 · International Conference on Pattern Recognition
43. Informed Democracy: Voting-based Novelty Detection for Action Recognition
    Alina Roitberg, Ziad Al-Halah, Rainer Stiefelhagen · 2018 · arXiv (Cornell University)
44. Label-Efficient Point Cloud Semantic Segmentation: An Active Learning Approach
    Xian Cheng Shi, Xun Xu, Ke Chen, Lile Cai, Chuan-Sheng Foo, Kui Jia · 2021 · arXiv (Cornell University)
45. Uncertainty-Aware Data Aggregation for Deep Imitation Learning
    Yuchen Cui, David Isele, Scott Niekum, Kikuo Fujimura · 2019 · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings
46. Iterative Projection and Matching: Finding Structure-Preserving Representatives and Its Application to Computer Vision
    Alireza Zaeemzadeh, Mohsen Joneidi, Nazanin Rahnavard, Mubarak A. Shah · 2019 · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings
47. Transfer learning with chest X-rays for ER patient classification
    Jonathan Stubblefield, Mitchell Hervert, Jason Causey, Jake Qualls, Wei Dong, Lingrui Cai, Jennifer Fowler, Emily S. Bellis, Karl Walker, Jason H. Moore et al. · 2020 · Scientific Reports
48. Adversarial Representation Active Learning
    Ali Mottaghi, Serena Y. Yeung · 2019 · arXiv (Cornell University)
49. Deterministic Neural Networks with Inductive Biases Capture Epistemic and Aleatoric Uncertainty
    Jishnu Mukhoti, Andreas Kirsch, Joost van Amersfoort, Philip H. S. Torr, Yarin Gal · 2021 · arXiv (Cornell University)
50. Exploring the Efficiency of Batch Active Learning for Human-in-the-Loop Relation Extraction
    Ismini Lourentzou, Daniel Gruhl, Steve Welch · 2018 · International World Wide Web Conference (WWW)
51. Minority Class Oriented Active Learning for Imbalanced Datasets
    Umang Aggarwal, Adrian Popescu, Céline Hudelot · 2021 · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition
52. Active Learning for Deep Detection Neural Networks
    Hamed Habibi Aghdam, Abel González-García, Joost van de Weijer, Antonio Manuel López · 2019 · arXiv (Cornell University)
53. POLAR++: Active One-Shot Personalized Article Recommendation
    Zhengxiao Du, Jie Tang, Yuhui Ding · 2019 · IEEE Transactions on Knowledge and Data Engineering
54. Active Learning for Patch-Based Digital Pathology Using Convolutional Neural Networks to Reduce Annotation Costs
    Jacob Carse, Stephen James McKenna · 2019 · Lecture notes in computer science
55. Active Learning for Reducing Labeling Effort in Text Classification Tasks
    Pieter Floris Jacobs, Gideon Maillette de Buy Wenniger, Marco Wiering, Lambert Schomaker · 2022 · Communications in computer and information science
56. Domain-adaptive active learning for cost-effective virtual metrology modeling
    Jaewoong Shim, Seokho Kang · 2021 · Computers in Industry
57. Active Vision in the Era of Convolutional Neural Networks
    Dimitrios Gallos, Frank P. Ferrie · 2019 · Canadian Conference on Computer and Robot Vision
58. Building an Active Palmprint Recognition System
    Xuefeng Du, Dexing Zhong, Huikai Shao · 2019 · Proceedings - International Conference on Image Processing
59. A Mathematical Analysis of Learning Loss for Active Learning in Regression
    Megh Shukla, Shuaib Ahmed · 2021 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
60. Simultaneous Multi-View Object Recognition and Grasping in Open-Ended Domains
    Hamidreza Kasaei, Mohammadreza Kasaei, Georgios Tziafas, Sha Luo, Remo Sasso · 2024 · Journal of Intelligent & Robotic Systems
61. Efficient Multi-Task Auxiliary Learning: Selecting Auxiliary Data by Feature Similarity
    Po-Nien Kung, Sheng-Siang Yin, Yi‐Cheng Chen, Tse-Hsuan Yang, Yun-Nung Chen · 2021 · Conference on Empirical Methods in Natural Language Processing (EMNLP)
62. How to Improve Deep Learning based Pavement Distress Detection while Minimizing Human Effort
    Daniel Seichter, Markus Eisenbach, Ronny Stricker, Horst–Michael Groß · 2018 · IEEE International Conference on Automation Science and Engineering (CASE)
63. A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images
    Issam Laradji, Pau Rodríguez, Frédéric Branchaud-Charron, Keegan Lensink, Parmida Atighehchian, Parker, William, David Vázquez, Derek Nowrouzezahrai · 2020 · arXiv (Cornell University)
64. Cross-View Person Identification Based on Confidence-Weighted Human Pose Matching
    Guoqiang Liang, Xuguang Lan, Xingyu Chen, Kang Zheng, Song Wang, Nanning Zheng · 2019 · IEEE Transactions on Image Processing
65. Deep Active Learning for Text Classification with Diverse Interpretations
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66. Exploring Spatial Diversity for Region-Based Active Learning
    Lile Cai, Xun Xu, Lining Zhang, Chuan-Sheng Foo · 2021 · IEEE Transactions on Image Processing
67. Active Image Synthesis for Efficient Labeling
    Jialei Chen, Yujia Xie, Kan Wang, Chuck Zhang, Mani A. Vannan, Ben Wang, Zhen Yu Qian · 2020 · IEEE Transactions on Pattern Analysis and Machine Intelligence
68. Quality-driven deep active learning method for 3D brain MRI segmentation
    Zhenxi Zhang, Jie Li, Chun-Na Tian, Zhu-Si Zhong, Z. Jiao, Xin-Bo Gao · 2021 · Neurocomputing
69. Sparse Semi-Supervised Action Recognition with Active Learning
    Jingyuan Li, Eli Shlizerman · 2020 · arXiv (Cornell University)
70. Small Data's Big AI Potential
    Husanjot Chahal, Helen Toner, Ilya M. Rahkovsky · 2021
71. Active Class Incremental Learning for Imbalanced Datasets
    Eden Belouadah, Adrian Popescu, Umang Aggarwal, Léo Saci · 2020 · Lecture notes in computer science
72. Efficacy of Bayesian Neural Networks in Active Learning
    Vineeth Rakesh, Swayambhoo Jain · 2021 · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
73. Active Learning based on Data Uncertainty and Model Sensitivity
    Nutan Chen, Alexej Klushyn, Alexandros Paraschos, Djalel Benbouzid, Patrick van der Smagt · 2018 · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
74. An Ensemble Deep Active Learning Method for Intent Classification
    Leihan Zhang, Le Zhang · 2019
75. MaxiMin Active Learning in Overparameterized Model Classes
    Mina Karzand, Robert D. Nowak · 2020 · IEEE Journal on Selected Areas in Information Theory
76. Deep Active Learning via Open-Set Recognition
    Jaya Krishna Mandivarapu, Blake Camp, Rolando G. Estrada · 2022 · Frontiers in Artificial Intelligence
77. Frugal Learning for Interactive Satellite Image Change Detection
    H. Sahbi, Sebastien Deschamps, A. Stoian · 2021 · 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS
78. Single Shot Structured Pruning Before Training
    Joost van Amersfoort, Milad Alizadeh, Sebastian Farquhar, Nicholas D. Lane, Yarin Gal · 2020 · arXiv (Cornell University)
79. The Hidden Uncertainty in a Neural Networks Activations
    Janis Postels, Hermann Blum, Yannick Strümpler, César Cadena, Roland Siegwart, Luc Van Gool, Federico Tombari · 2020 · arXiv (Cornell University)
80. Low Budget Active Learning via Wasserstein Distance: An Integer Programming Approach
    Rafid Mahmood, Sanja Fidler, Marc T. Law · 2021 · arXiv (Cornell University)
81. Fogification of electric drives: An industrial use case
    Mohammadreza Barzegaran, Nitin Desai, Jia Qian, Koen Tange, Bahram Zarrin, Paul Pop, Juha Kuusela · 2020 · IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)
82. Uncertainty-Aware Data Augmentation for Food Recognition
    Eduardo Aguilar, Bhalaji Nagarajan, Rupali Khantun, Marc Bolaños, Petia I. Radeva · 2021 · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition
83. Deep Bayesian Active Learning for Multiple Correct Outputs
    Khaled Jedoui, Ranjay Krishna, Michael S. Bernstein, Li Fei-Fei · 2019 · arXiv (Cornell University)
84. Scalable Uncertainty for Computer Vision With Functional Variational Inference
    Carvalho, Eduardo D. C., Clark, Ronald, Nicastro, Andrea, Kelly, Paul H. J. · 2020 · 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
85. Labeling Cost Sensitive Batch Active Learning For Brain Tumor Segmentation
    Maohao Shen, Jacky Y. Zhang, Leihao Chen, Weiman Yan, Neel D. Jani, Bradley P. Sutton, Sanmi Koyejo · 2021 · IEEE International Symposium on Biomedical Imaging (ISBI)
86. Deep Hashing with Active Pairwise Supervision
    Ziwei Wang, Quan Shui Zheng, Jiwen Lu, Jie Zhou · 2020 · Lecture notes in computer science
87. Diversity-Aware Batch Active Learning for Dependency Parsing
    Tianze Shi, Adrian Benton, Igor Malioutov, Ozan İrsoy · 2021 · Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)
88. Automatic Acoustic Mosquito Tagging with Bayesian Neural Networks
    Ivan Kiskin, Adam D. Cobb, Marianne E Sinka, Katherine J. Willis, Stephen Roberts · 2021 · Lecture notes in computer science
89. A deep active learning-based and crowdsourcing-assisted solution for named entity recognition in Chinese historical corpora
    Chengxi Yan, Xuemei Tang, Hao Yang, Jun Wang · 2022 · Aslib Journal of Information Management
90. Bayesian Deep Learning and Uncertainty in Computer Vision
    Buu Phan · 2019 · UWSpace (University of Waterloo)
91. Calibration of Model Uncertainty for Dropout Variational Inference
    Laves, Max-Heinrich, Ihler, Sontje, Kortmann, Karl-Philipp, Ortmaier, Tobias · 2020 · arXiv
92. Bayesian active learning for production, a systematic study and a reusable library
    Parmida Atighehchian, Frédéric Branchaud-Charron, Alexandre Lacoste · 2020 · arXiv (Cornell University)
93. On uncertainty estimation in active learning for image segmentation
    Bo Li, T. Alstrøm · 2020 · arXiv.org
94. Discrepancy-Based Active Learning for Domain Adaptation
    Antoine de Mathelin, François Deheeger, Mathilde Mougeot, Nicolas Vayatis · 2021 · arXiv (Cornell University)
95. Deep Active Learning for Sequence Labeling Based on Diversity and Uncertainty in Gradient
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96. Toward implementing autonomous adaptive data acquisition for scanning hyperspectral imaging of biological systems
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97. Integrating Informativeness, Representativeness and Diversity in Pool-Based Sequential Active Learning for Regression
    Ziang Liu, Dongrui Wu · 2020 · International Joint Conference on Neural Networks (IJCNN)
98. Annotation Curricula to Implicitly Train Non-Expert Annotators
    Ji-Ung Lee, Jan-Christoph Klie, Iryna Gurevych · 2022 · Computational Linguistics
99. Agreement-Discrepancy-Selection: Active Learning with Progressive Distribution Alignment
    Mengying Fu, Tianning Yuan, Fang Wan, Songcen Xu, Qixiang Ye · 2021 · Proceedings of the AAAI Conference on Artificial Intelligence
100. Icebreaker: Element-wise Active Information Acquisition with Bayesian Deep Latent Gaussian Model
     Wenbo Gong, Sebastian Tschiatschek, Richard E. Turner, Sebastian Nowozin, José Miguel Hernández-Lobato, Cheng Zhang · 2019 · arXiv (Cornell University)