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Human-in-the-loop aims to train an accurate prediction model with minimum cost by integrating human knowledge and experience.
Y. Zhuang, G. Li, Z. Zhong, and J. Feng, “Hike: A hybrid human-machine method for entity alignment in large-scale knowledge bases,” in
1926
Earlier work this paper cites.
M. R. Banham and A. K. Katsaggelos, “Digital image restoration,”
1997
Earlier work this paper cites.
S. Brostoff and M. A. Sasse, “Safe and sound: a safety-critical approach to security,” in
2001
Earlier work this paper cites.
A. Criminisi, P. Perez, and K. Toyama, “Object removal by exemplar-based inpainting,” in
2003
Earlier work this paper cites.
C. Burges, T. Shaked, E. Renshaw, A. Lazier, M. Deeds, N. Hamilton, and G. Hullender, “Learning to rank using gradient descent,” in
2005
Earlier work this paper cites.
L. F. Cranor, “A framework for reasoning about the human in the loop,” in
2008
Earlier work this paper cites.
B. Settles, “Closing the loop: Fast, interactive semi-supervised annotation with queries on features and instances,” in
2011
Earlier work this paper cites.
A. Yao, J. Gall, C. Leistner, and L. Van Gool, “Interactive object detection,” in
2012
Earlier work this paper cites.
A. Machiry, R. Tahiliani, and M. Naik, “Dynodroid: An input generation system for android apps,” in
2013
Earlier work this paper cites.
Y. Fu, X. Zhu, and B. Li, “A survey on instance selection for active learning,”
2013
Earlier work this paper cites.
B. Zhou, A. Lapedriza, J. Xiao, A. Torralba, and A. Oliva, “Learning deep features for scene recognition using places database,” in
2014
Earlier work this paper cites.
A. Kapoor, J. C. Caicedo, D. Lischinski, and S. B. Kang, “Collaborative personalization of image enhancement,”
2014
Earlier work this paper cites.
F. Yu, A. Seff, Y. Zhang, S. Song, T. Funkhouser, and J. Xiao, “Lsun: Construction of a large-scale image dataset using deep learning with humans in the loop,” 2015
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,”
2015
Earlier work this paper cites.
R. Girshick, “Fast r-cnn,” in
2015
Earlier work this paper cites.
A. Kovashka, D. Parikh, and K. Grauman, “Whittlesearch: Interactive image search with relative attribute feedback,”
2015
Earlier work this paper cites.
L. He, J. Michael, M. Lewis, and L. Zettlemoyer, “Human-in-the-loop parsing,” in
2016
Earlier work this paper cites.
J. Z. Self, R. K. Vinayagam, J. Fry, and C. North, “Bridging the gap between user intention and model parameters for human-in-the-loop data analytics,” in
2016
Earlier work this paper cites.
H. Ye, W. Shao, H. Wang, J. Ma, L. Wang, Y. Zheng, and X. Xue, “Face recognition via active annotation and learning,” in
2016
Earlier work this paper cites.
S. Chopra, M. Auli, and A. M. Rush, “Abstractive sentence summarization with attentive recurrent neural networks,” in
2016
Earlier work this paper cites.
M. T. Ribeiro, S. Singh, and C. Guestrin, “” why should i trust you?” explaining the predictions of any classifier,” in
2016
Earlier work this paper cites.
N. Xu, B. Price, S. Cohen, J. Yang, and T. S. Huang, “Deep interactive object selection,” in
2016
Earlier work this paper cites.
L. Rosenberg, “Artificial swarm intelligence, a human-in-the-loop approach to ai,” in
2016
Earlier work this paper cites.
P. Wiriyathammabhum, D. Summers-Stay, C. Fermüller, and Y. Aloimonos, “Computer vision and natural language processing: recent approaches in multimedia and robotics,”
2016
Earlier work this paper cites.
J. Li, A. H. Miller, S. Chopra, M. Ranzato, and J. Weston, “Dialogue learning with human-in-the-loop,”
2016
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in
2017
Earlier work this paper cites.
M. Diligenti, S. Roychowdhury, and M. Gori, “Integrating prior knowledge into deep learning,” in
2017
Earlier work this paper cites.
Y.-t. Zhuang, F. Wu, C. Chen, and Y.-h. Pan, “Challenges and opportunities: from big data to knowledge in ai 2.0,”
2017
Earlier work this paper cites.
G. Li, “Human-in-the-loop data integration,”
2017
Earlier work this paper cites.
N. Wojke, A. Bewley, and D. Paulus, “Simple online and realtime tracking with a deep association metric,” in
2017
Earlier work this paper cites.
V. Badrinarayanan, A. Kendall, and R. Cipolla, “Segnet: A deep convolutional encoder-decoder architecture for image segmentation,”
2017
Earlier work this paper cites.
A. Benard and M. Gygli, “Interactive video object segmentation in the wild,” 2017
2017
Earlier work this paper cites.
K. N. Shukla, A. Potnis, and P. Dwivedy, “A review on image enhancement techniques,”
2017
Earlier work this paper cites.
S. Caelles, K.-K. Maninis, J. Pont-Tuset, L. Leal-Taixé, D. Cremers, and L. Van Gool, “One-shot video object segmentation,” in
2017
Earlier work this paper cites.
Y. Shoshitaishvili, M. Weissbacher, L. Dresel, C. Salls, R. Wang, C. Kruegel, and G. Vigna, “Rise of the hacrs: Augmenting autonomous cyber reasoning systems with human assistance,” in
2017
Earlier work this paper cites.
A. Doan, A. Ardalan, J. Ballard, S. Das, Y. Govind, P. Konda, H. Li, S. Mudgal, E. Paulson, G. P. Suganthan
2017
Earlier work this paper cites.
J. Zhang, P. Fiers, K. A. Witte, R. W. Jackson, K. L. Poggensee, C. G. Atkeson, and S. H. Collins, “Human-in-the-loop optimization of exoskeleton assistance during walking,”
2017
Earlier work this paper cites.
T. Y. Lee, A. Smith, K. Seppi, N. Elmqvist, J. Boyd-Graber, and L. Findlater, “The human touch: How non-expert users perceive, interpret, and fix topic models,”
2017
Earlier work this paper cites.
A. Radford, K. Narasimhan, T. Salimans, and I. Sutskever, “Improving language understanding by generative pre-training,” 2018
2018
Earlier work this paper cites.
X. Zhang, S. Wang, J. Liu, and C. Tao, “Towards improving diagnosis of skin diseases by combining deep neural network and human knowledge,”
2018
Earlier work this paper cites.
D. Xin, L. Ma, J. Liu, S. Macke, S. Song, and A. Parameswaran, “Accelerating human-in-the-loop machine learning: Challenges and opportunities,” in
2018
Earlier work this paper cites.
G. Wang, W. Li, M. A. Zuluaga, R. Pratt, P. A. Patel, M. Aertsen, T. Doel, A. L. David, J. Deprest, S. Ourselin
2018
Earlier work this paper cites.
B. Kim and B. Pardo, “A human-in-the-loop system for sound event detection and annotation,”
2018
Earlier work this paper cites.
A. Doan, “Human-in-the-loop data analysis: a personal perspective,” in
2018
Earlier work this paper cites.
X. L. Dong and T. Rekatsinas, “Data integration and machine learning: A natural synergy,” in
2018
Earlier work this paper cites.
B. Nushi, E. Kamar, and E. Horvitz, “Towards accountable ai: Hybrid human-machine analyses for characterizing system failure,” in
2018
Earlier work this paper cites.
G. Liu, F. A. Reda, K. J. Shih, T.-C. Wang, A. Tao, and B. Catanzaro, “Image inpainting for irregular holes using partial convolutions,” in
2018
Earlier work this paper cites.
D. Ulyanov, A. Vedaldi, and V. Lempitsky, “Deep image prior,” in
2018
Earlier work this paper cites.
X. Fu, J. Yan, and C. Fan, “Image aesthetics assessment using composite features from off-the-shelf deep models,” in
2018
Earlier work this paper cites.
M. S. Wogalter, “Communication-human information processing (c-hip) model,” in
2018
Earlier work this paper cites.
L. Ma, “Towards understanding and simplifying human-in-the-loop machine learning,” pp. 1–1, 2018
2018
Earlier work this paper cites.
A. Smith, V. Kumar, J. Boyd-Graber, K. Seppi, and L. Findlater, “Closing the loop: User-centered design and evaluation of a human-in-the-loop topic modeling system,” in
2018
Earlier work this paper cites.
J. J. Dudley and P. O. Kristensson, “A review of user interface design for interactive machine learning,”
2018
Earlier work this paper cites.
K. Shilton, “Values and ethics in human-computer interaction,”
2018
Earlier work this paper cites.
A. Brutzkus and A. Globerson, “Why do larger models generalize better? a theoretical perspective via the xor problem,” in
2019
Cited alongside, same era.
J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “BERT: Pre-training of deep bidirectional transformers for language understanding,” in
2019
Cited alongside, same era.
G. Hartmann, Z. Shiller, and A. Azaria, “Deep reinforcement learning for time optimal velocity control using prior knowledge,” in
2019
Cited alongside, same era.
R. Zhang, F. Torabi, L. Guan, D. H. Ballard, and P. Stone, “Leveraging human guidance for deep reinforcement learning tasks,” in
2019
Cited alongside, same era.
A. Holzinger, M. Plass, M. Kickmeier-Rust, K. Holzinger, G. C. Crişan, C.-M. Pintea, and V. Palade, “Interactive machine learning: experimental evidence for the human in the algorithmic loop,”
2019
K. Madono, T. Nakano, T. Kobayashi, and T. Ogawa, “Efficient human-in-the-loop object detection using bi-directional deep sort and annotation-free segment identification,” in
2020
Later among the works it cites.
T. Weber, H. Hußmann, Z. Han, S. Matthes, and Y. Liu, “Draw with me: Human-in-the-loop for image restoration,” in
2020
Later among the works it cites.
H. Wang, T. Chen, Z. Wang, and K. Ma, “Efficiently troubleshooting image segmentation models with human-in-the-loop,” pp. 1–1, 2020
2020
Later among the works it cites.
M. Ravanbakhsh, V. Tschernezki, F. Last, T. Klein, K. Batmanghelich, V. Tresp, and M. Nabi, “Human-machine collaboration for medical image segmentation,” in
2020
Later among the works it cites.
M. Fischer, K. Kobs, and A. Hotho, “Nicer: Aesthetic image enhancement with humans in the loop,” in
2020
Later among the works it cites.
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Cited alongside, same era.
V. Kumar, A. Smith-Renner, L. Findlater, K. Seppi, and J. Boyd-Graber, “Why didn’t you listen to me? comparing user control of human-in-the-loop topic models,” in
2019
Cited alongside, same era.
W. Jung and F. Jazizadeh, “Human-in-the-loop hvac operations: A quantitative review on occupancy, comfort, and energy-efficiency dimensions,”
2019
Cited alongside, same era.
S. Agnisarman, S. Lopes, K. C. Madathil, K. Piratla, and A. Gramopadhye, “A survey of automation-enabled human-in-the-loop systems for infrastructure visual inspection,”
2019
Cited alongside, same era.
B. M. Tehrani, J. Wang, and C. Wang, “Review of human-in-the-loop cyber-physical systems (hilcps): The current status from human perspective,”
2019
Cited alongside, same era.
A. L. Gentile, D. Gruhl, P. Ristoski, and S. Welch, “Explore and exploit. dictionary expansion with human-in-the-loop,” in
2019
Cited alongside, same era.
S. Zhang, L. He, E. Dragut, and S. Vucetic, “How to invest my time: Lessons from human-in-the-loop entity extraction,” in
2019
Cited alongside, same era.
L. Berti-Equille, “Reinforcement learning for data preparation with active reward learning,” in
2019
Cited alongside, same era.
R. Yao, G. Lin, S. Xia, J. Zhao, and Y. Zhou, “Video object segmentation and tracking: A survey,”
2020
Later among the works it cites.
H. V. Singh and Q. H. Mahmoud, “Human-in-the-loop error precursor detection using language translation modeling of hmi states,” in
2020
Later among the works it cites.
G. Demartini, S. Mizzaro, and D. Spina, “Human-in-the-loop artificial intelligence for fighting online misinformation: Challenges and opportunities,”
2020
Later among the works it cites.
D. Odekerken and F. Bex, “Towards transparent human-in-the-loop classification of fraudulent web shops,” in
2020
Later among the works it cites.
M. Böhme, C. Geethal, and V.-T. Pham, “Human-in-the-loop automatic program repair,” in
2020
Later among the works it cites.
A. Renner, “Designing for the human in the loop: Transparency and control in interactive machine learning,” Ph.D. dissertation, University of Maryland, College Park, 2020
2020
Later among the works it cites.
H. O. Demirel, “Digital human-in-the-loop framework,” in
2020
Later among the works it cites.
M. Metzner, D. Utsch, M. Walter, C. Hofstetter, C. Ramer, A. Blank, and J. Franke, “A system for human-in-the-loop simulation of industrial collaborative robot applications,” in
2020
Later among the works it cites.
A. Polisetty Venkata Sai, “Information preparation with the human in the loop,” Ph.D. dissertation, TU Darmstadt, 2020
2020
Later among the works it cites.
Z. Zhu, Y. Lu, R. Deng, H. Yang, A. B. Fogo, and Y. Huo, “Easierpath: An open-source tool for human-in-the-loop deep learning of renal pathology,” in
2020
Later among the works it cites.
N. Li, S. Adepu, E. Kang, and D. Garlan, “Explanations for human-on-the-loop: A probabilistic model checking approach,” in
2020
Later among the works it cites.
L. Yang, Q. Sun, N. Zhang, and Z. Liu, “Optimal energy operation strategy for we-energy of energy internet based on hybrid reinforcement learning with human-in-the-loop,”
2020
Later among the works it cites.
Y. Tay, M. Dehghani, D. Bahri, and D. Metzler, “Efficient transformers: A survey,” 2020
2020
Later among the works it cites.
S. Dong, P. Wang, and K. Abbas, “A survey on deep learning and its applications,”
2021
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J. Li, J. Yang, A. Hertzmann, J. Zhang, and T. Xu, “LayoutGAN: Synthesizing graphic layouts with vector-wireframe adversarial networks,”
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H. T. Shen, X. Zhu, Z. Zhang, S.-H. Wang, Y. Chen, X. Xu, and J. Shao, “Heterogeneous data fusion for predicting mild cognitive impairment conversion,”
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S. Bahrami, F. Dornaika, and A. Bosaghzadeh, “Joint auto-weighted graph fusion and scalable semi-supervised learning,”
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S. Jia, S. Jiang, Z. Lin, N. Li, M. Xu, and S. Yu, “A survey: Deep learning for hyperspectral image classification with few labeled samples,”
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S. Budd, E. C. Robinson, and B. Kainz, “A survey on active learning and human-in-the-loop deep learning for medical image analysis,”
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Z. Y. Khan, Z. Niu, S. Sandiwarno, and R. Prince, “Deep learning techniques for rating prediction: a survey of the state-of-the-art,”
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O. Siméoni, M. Budnik, Y. Avrithis, and G. Gravier, “Rethinking deep active learning: Using unlabeled data at model training,” in
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Y. Wang, L. Zhang, Y. Yao, and Y. Fu, “How to trust unlabeled data instance credibility inference for few-shot learning,”
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Y. Shi and A. K. Jain, “Boosting unconstrained face recognition with auxiliary unlabeled data,” in
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S. Khan, M. Naseer, M. Hayat, S. W. Zamir, F. S. Khan, and M. Shah, “Transformers in vision: A survey,”
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T. D. Pham, “Classification of covid-19 chest x-rays with deep learning: new models or fine tuning?”
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K. Muthuraman, F. Reiss, H. Xu, B. Cutler, and Z. Eichenberger, “Data cleaning tools for token classification tasks,” in
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Q. Meng, W. Wang, T. Zhou, J. Shen, Y. Jia, and L. Van Gool, “Towards a weakly supervised framework for 3d point cloud object detection and annotation,”
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L. Zhang, X. Wang, Q. Fan, Y. Ji, and C. Liu, “Generating manga from illustrations via mimicking manga creation workflow,” in
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B. Adhikari and H. Huttunen, “Iterative bounding box annotation for object detection,” in
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I. Arous, L. Dolamic, J. Yang, A. Bhardwaj, G. Cuccu, and P. Cudré-Mauroux, “Marta: Leveraging human rationales for explainable text classification,” in
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Z. Liu, Y. Guo, A. A. AI, and J. Mahmud, “When and why does a model fail? a human-in-the-loop error detection framework for sentiment analysis,”
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Z. J. Wang, D. Choi, S. Xu, and D. Yang, “Putting humans in the natural language processing loop: A survey,” in
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X. Wu, Y. Zheng, T. Ma, H. Ye, and L. He, “Document image layout analysis via explicit edge embedding network,”
2021
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S. Minaee, Y. Y. Boykov, F. Porikli, A. J. Plaza, N. Kehtarnavaz, and D. Terzopoulos, “Image segmentation using deep learning: A survey,”
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A. Taleb, C. Lippert, T. Klein, and M. Nabi, “Multimodal self-supervised learning for medical image analysis,” in
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M. Hudec, E. Mináriková, R. Mesiar, A. Saranti, and A. Holzinger, “Classification by ordinal sums of conjunctive and disjunctive functions for explainable ai and interpretable machine learning solutions,”
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J. B. Davidson, R. B. Graham, S. Beck, R. T. Marler, and S. L. Fischer, “Improving human-in-the-loop simulation to optimize soldier-systems integration,”
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A. Holzinger, B. Malle, A. Saranti, and B. Pfeifer, “Towards multi-modal causability with graph neural networks enabling information fusion for explainable ai,”
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S. Arora and P. Doshi, “A survey of inverse reinforcement learning: Challenges, methods and progress,”
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H. Amirpourazarian, A. Pinheiro, E. Fonseca, M. Ghanbari, and M. Pereira, “Quality evaluation of holographic images coded with standard codecs,”
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S. Wan, Y. Hou, F. Bao, Z. Ren, Y. Dong, Q. Dai, and Y. Deng, “Human-in-the-loop low-shot learning,”
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J. Kreutzer, S. Riezler, and C. Lawrence, “Offline reinforcement learning from human feedback in real-world sequence-to-sequence tasks,” in
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J.-S. Jwo, C.-S. Lin, and C.-H. Lee, “Smart technology–driven aspects for human-in-the-loop smart manufacturing,”
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N. M. Marquand, “Automated modeling of human-in-the-loop systems,” Ph.D. dissertation, Purdue University Graduate School, 2021
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K. Zhou, Z. Liu, Y. Qiao, T. Xiang, and C. Change Loy, “Domain generalization: A survey,” 2021
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A. Jolfaei, M. Usman, M. Roveri, M. Sheng, M. Palaniswami, and K. Kant, “Guest editorial: Computational intelligence for human-in-the-loop cyber physical systems,”
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W. Xu, M. J. Dainoff, L. Ge, and Z. Gao, “Transitioning to human interaction with ai systems: New challenges and opportunities for hci professionals to enable human-centered ai,”
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B. A. Plummer, M. H. Kiapour, S. Zheng, and R. Piramuthu, “Give me a hint! navigating image databases using human-in-the-loop feedback,” in
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