Cited by 740Machine Learning and AlgorithmsMachine Learning and Data ClassificationDomain Adaptation and Few-Shot Learning

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Multiple instance learning: A survey of problem characteristics and applications

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Carbonneau, Marc-André, Cheplygina, Veronika, Granger, Eric, Gagnon, Ghyslain·2018·Pattern Recognition

DOI 10.1016/j.patcog.2017.10.009

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ImageNet Large Scale Visual Recognition Challenge
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SMOTE synthetic minority over-sampling technique
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“There are external methods like SMOTE [193] and RUSBoost [194] that necessitate accurate labels to perform over or under sampling.”
Method
Greedy function approximation: A gradient boosting machine
Friedman, J. H. (2001) · The Annals of Statistics · doi:10.1214/aos/1013203451 · Cited by 30,731 · cited 1×
“The method is essentially the same as gradient boosting [143] except that the loss function is based on bag classification error.”
Method
The pascal visual object classes (VOC) challenge
Everingham, M., Van Gool, L., Williams, C. K. I., Winn, J., & Zisserman, A. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0275-4 · Cited by 19,599
The WEKA data mining software: an update
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Representation Learning: A Review and New Perspectives
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“Recently, methods for representation learning [160] have gained in popularity because they usually yield a high level of accuracy.”
Method
Statistical Comparisons of Classifiers over Multiple Data Sets
Demšar, J. (2006) · doi:10.5555/1248547.1248548 · Cited by 12,051
Cross validatory choice and assessment of statistical predictions
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“The experiments were conducted using a nested cross-fold validation protocol [149].”
Method
Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories
Lazebnik, S., Schmid, C., & Ponce, J. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.68 · Cited by 7,916 · cited 1×
“In BoW models for computer vision, this can be achieved by dividing the images [102, 103] or videos [80] into different spatial and/or temporal zones.”
The Earth Mover's Distance as a Metric for Image Retrieval
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Babenko, 2011, Robust object tracking with online multiple instance learning, IEEE Trans. Pattern Anal. Mach. Intell., 33, 1619, 10.1109/TPAMI.2010.226 · DOI · Paper page

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“We refer the readers to [4] for a survey.”
Background
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“…that the label Y = 0 is noiseless, since it means all snippets v i in the video V are normal: However, Y = 1 is noisy because in this case the video V is partially made up of anomalous clips: This is referred to as one-sided label noise [7, 9, 55], for the noise only appears along with Y = 1 .”
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“In training bags, image segmentation and feature extraction are performed, based on the size of dictionary bag features computed and finally applied, a classifier; the model predicts the result [30].”
Method
Hopfield Networks is All You Need
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“With over 30 billion sequences in total, this represents one of the largest multiple instance learning experiments ever conducted [15] (for details see [70], appendix, Section A2).”
Background
Pooling in convolutional neural networks for medical image analysis: a survey and an empirical study
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Method
Weakly supervised machine learning
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“In breast cancer imaging, the most used weakly-supervised learning methods are class activation map (CAM) [61] and multiple instancelearning(MIL)[62].”
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“There aremanystudies [4], [5], [6], [7], [8],”
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“Moreover, MOMA revealed the previously unknown correlation between histopathology image patterns and the expression levels of BECN1 (Supplemental Figure 2A), with the results validated in the NHS-HPFS dataset (Supplemental Figure 2B and Supplemental Table 1).”
A multi-resolution model for histopathology image classification and localization with multiple instance learning
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“However, in MIL problems considered by machine learning methods up to now, the number of instances per bag is in the range of hundreds or few thousands (Carbonneau et al., 2018; Lee et al., 2019) (see also Tab.”
A systematic review of deep learning-based cervical cytology screening: from cell identification to whole slide image analysis
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“Mapping pertinent receptors in a repertoire to a single phenotype label can formally be described as MIL, which treats problems as bags of instances where the bags are labeled but the instances are not (24).”
Background
Representation Learning of Histopathology Images using Graph Neural Networks
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“[3].”
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
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“Our multi-instance pose network is fully supervised and not related to multiple instance learning [4, 46], which is a form of weakly-supervised learning paradigm where training instances are arranged in sets.”
Background
Deep Learning-Based Action Detection in Untrimmed Videos: A Survey
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“are arranged in sets, called bags, and a label is provided for the entire bag [67].”
Background
Deep computational pathology in breast cancer
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“The authors employed (for the first time in computational pathology) a learning method termed Multiple Instance Learning (MIL) (see [117] for a review).”
Method
Artificial intelligence applications in prostate cancer
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“Readers can refer Carbonneau et al. (2018) for a comprehensive survey.”
Background
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Self-citation
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“The conventional MIL implementations were used from [49].”
Method
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“A natural future extension of this work would be to investigate the behavior of indices which are used in imbalanced multi-label [41] and multi-instance [42] classiﬁcation problems.”
Background
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“Multiple instance learning [5] is commonly used to train the classifier, where top-k pooling is applied over CAS for each class to aggregate the highest”
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“A good survey on the state-of-the-art MIL algorithms, their characteristics and challenges, along with several applications, is provided in Carbonneau et al. (2018).”
Background
Deep Weakly-Supervised Learning Methods for Classification and Localization in Histology Images: A Survey
Rony, J., Belharbi, S., Dolz, J., Ayed, I. B., McCaffrey, L. M., & Granger, É. (2023) · The Journal of Machine Learning for Biomedical Imaging · doi:10.59275/j.melba.2023-5g54 · Cited by 83
“Under this scenario, powerful techniques for multiple-instance learning (MIL) (Carbonneau et al., 2018; Cheplygina et al., 2019; Wang et al., 2018; Zhou, 2004) are generally considered, where individual instance labels (e.g . image pixels, segments or patches) are not observable or do not belong to…”
Method
Temporal Cue Guided Video Highlight Detection with Low-Rank Audio-Visual Fusion
Ye, Q., Shen, X., Gao, Y., Wang, Z., Bi, Q., Li, P., & Yang, G. (2021) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00785 · Cited by 60
“Early MIL related works [2, 4] pointed out that simply applying the video label to segment label might be inaccurate since labels in positive video could be noisy.”
Background
Visualizing the outcome of dynamic analysis of Android malware with VizMal
Lorenzo, A. D., Martinelli, F., Medvet, E., Mercaldo, F., & Santone, A. (2019) · Journal of Information Security and Applications · doi:10.1016/j.jisa.2019.102423 · Cited by 59
“Another approach which can fit our scenario is the Multiple Instance Learning (MIL) framework [22, 70].”
Background
Digital mapping of heavy metals in urban soils: A review and research challenges
Shi, T., Li, H., Wang, R., Li, Z., Hu, Z., & Wu, G. (2023) · CATENA · doi:10.1016/j.catena.2023.107183 · Cited by 58
Artificial intelligence in COPD CT images: identification, staging, and quantitation
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“MIL is a weakly supervised learning paradigm where the training data consists of labeled bags, each containing multiple instances [55].”
Background
Multiple Instance Learning for Emotion Recognition Using Physiological Signals
Romeo, L., Cavallo, A., Pepa, L., Bianchi‐Berthouze, N., & Pontil, M. (2019) · IEEE Transactions on Affective Computing · doi:10.1109/taffc.2019.2954118 · Cited by 58
“MIL has attracted much attention from the research community, especially in recent years, as the amount of data needed to learn difficult tasks has increased exponentially [26].”
Background
Learning to select cuts for efficient mixed-integer programming
Huang, Z., Wang, K., Liu, F., Zhen, H., Zhang, W., Yuan, M., Hao, J., Yu, Y. H., & Wang, Y.-X. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108353 · Cited by 56
Causality-Driven Graph Neural Network for Early Diagnosis of Pancreatic Cancer in Non-Contrast Computerized Tomography
Li, X., Guo, R., Lu, J., Chen, T., & Qian, X. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3236162 · Cited by 56
“The MIL has been widely employed in many computer vision tasks [15], where this framework generally exhibited good feature representation capabilities [12], [16], [17], [18].”
Method
MRI-based Alzheimer’s disease prediction via distilling the knowledge in multi-modal data
Guan, H., Wang, C., & Tao, D. (2021) · NeuroImage · doi:10.1016/j.neuroimage.2021.118586 · Cited by 58
“set contains multiple unlabelled instances [1, 9].”
Accounting for Dependencies in Deep Learning Based Multiple Instance Learning for Whole Slide Imaging
Myronenko, A., Xu, Z., Yang, D., Roth, H. R., & Xu, D. (2021) · Lecture notes in computer science · doi:10.1007/978-3-030-87237-3_32 · Cited by 55
Multilabel Appliance Classification With Weakly Labeled Data for Non-Intrusive Load Monitoring
Tanoni, G., Principi, E., & Squartini, S. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3191908 · Cited by 55
“Learning from bags raises important challenges that are unique to MIL formulation [43].”
Background
AdvMIL: Adversarial multiple instance learning for the survival analysis on whole-slide images
Liu, P., Ji, L., Ye, F., & Fu, B. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.103020 · Cited by 63
Machine learning and related approaches in transcriptomics
Cheng, Y., Xu, S.-M., Santucci, K., Lindner, G., & Janitz, M. (2024) · Biochemical and Biophysical Research Communications · doi:10.1016/j.bbrc.2024.150225 · Cited by 55
Masked Conditional Neural Networks for sound classification
Medhat, F., Chesmore, D., & Robinson, J. (2020) · Applied Soft Computing · doi:10.1016/j.asoc.2020.106073 · Cited by 56
“The evaluation of the MCLNN for sound classification follows the Multiple Instance Learning paradigm [56] used in most of the attempts referenced in the literature, where the original tag of the sound file is used to label each segment in isolation and the final decision for the clip follows a…”
Method
DeepAIR: A deep learning framework for effective integration of sequence and 3D structure to enable adaptive immune receptor analysis
(2023) · doi:10.1126/sciadv.abo5128 · Cited by 53
“The repertoire-level analysis task can be formulated as a typical MIL problem ( 55 ), where each repertoire can be regarded as a bag containing receptors that are instances.”
Deep multiple-instance learning for abnormal cell detection in cervical histopathology images
Pal, A., Xue, Z., Desai, K. T., Banjo, A. A. F., Adepiti, C. A., Long, L. R., SCHIFFMAN, M. H., & Antani, S. (2021) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2021.104890 · Cited by 55
Kernel Self-Attention for Weakly-supervised Image Classification using Deep Multiple Instance Learning
Rymarczyk, D., Borowa, A., Tabor, J., & Zieliński, B. (2021) · IEEE Winter Conference on Applications of Computer Vision · doi:10.1109/wacv48630.2021.00176 · Cited by 62
“MIL problems are common in medical image analysis due to the vast resolution of images and the weakly-labeled small datasets [4, 21].”
Background
Unobtrusive detection of Parkinson’s disease from multi-modal and in-the-wild sensor data using deep learning techniques
Papadopoulos, A., Iakovakis, D., Klingelhoefer, L., Bostantjopoulou, S., Chaudhuri, К. R., Kyritsis, K., Hadjidimitriou, S., Charisis, V. S., Hadjileontiadis, L. J., & Delopoulos, A. N. (2020) · Scientific Reports · doi:10.1038/s41598-020-78418-8 · Cited by 52
See and Read: Detecting Depression Symptoms in Higher Education Students Using Multimodal Social Media Data
Mann, P., Paes, A., & Matsushima, E. H. (2020) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v14i1.7313 · Cited by 56
Real-time incident prediction for online service systems
Zhao, N., Chen, J., Wang, Z., Peng, X., Wang, G., Wu, Y., Zhou, F., Feng, Z., Nie, X., Zhang, W., et al. (2020) · ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE) · doi:10.1145/3368089.3409672 · Cited by 59
“To reduce the influence of noisy alerts, eWarn adopts multi-instance learning (MIL) [11].”
BackgroundMethodInfluential
WeGleNet: A weakly-supervised convolutional neural network for the semantic segmentation of Gleason grades in prostate histology images
Silva-Rodríguez, J., Colomer, A., & Naranjo, V. (2021) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2020.101846 · Cited by 52
“A recent extensive review of MIL and its characteristics can be found in [6].”
Background
Discrepant multiple instance learning for weakly supervised object detection
Gao, W., Wan, F., Yue, J., Xu, S., & Ye, Q. (2021) · Pattern Recognition · doi:10.1016/j.patcog.2021.108233 · Cited by 54
A Comprehensive Review on Multiple Instance Learning
Fatima, S., Ali, S., & Kim, H. (2023) · Electronics · doi:10.3390/electronics12204323 · Cited by 51
Dual attention multiple instance learning with unsupervised complementary loss for COVID-19 screening
Chikontwe, P., Luna, M., Kang, M., Hong, K. S., Ahn, J. H., & Park, S. H. (2021) · Medical Image Analysis · doi:10.1016/j.media.2021.102105 · Cited by 54
Decouple and Resolve: Transformer-Based Models for Online Anomaly Detection From Weakly Labeled Videos
Liu, T., Zhang, C., Lam, K., & Kong, J. (2022) · IEEE Transactions on Information Forensics and Security · doi:10.1109/tifs.2022.3216479 · Cited by 50
“Wan et al. [23] leverage a center loss to cluster normal instances, which is complementary to the conventional MIL principle for tackling the WS-VAD task.”
BackgroundInfluential
Multi-instance learning of graph neural networks for aqueous p K a prediction
Xiong, J., Li, Z. S., Wang, G., Fu, Z., Zhong, F., Xu, T., Liu, X., Huang, Z., Liu, X., Chen, K., et al. (2021) · Bioinformatics · doi:10.1093/bioinformatics/btab714 · Cited by 52
Golden Standard or Obsolete Method? Review of ECG Applications in Clinical and Experimental Context
Stračina, T., Ronzhina, M., Ředina, R., & Novàkovâ, M. (2022) · Frontiers in Physiology · doi:10.3389/fphys.2022.867033 · Cited by 56
“MIL is a type of supervised learning, which works with labeled bags of data (instead of the labeled instances) (Carbonneau et al., 2018).”
Background
DCT-MIL: Deep CNN transferred multiple instance learning for COPD identification using CT images
Xu, C., Qi, S., Feng, J., Xia, S., Kang, Y., Yao, Y., & Qian, W. (2020) · Physics in Medicine and Biology · doi:10.1088/1361-6560/ab857d · Cited by 47
“Second, as one method of MIL, Citation-KNN also has the advantages similar to the image retrieval and ensemble learning (Carbonneau et al 2016).”
BackgroundMethod
A survey of class-imbalanced semi-supervised learning
Gui, Q., Hong, Z., Guo, N., & Niu, B. (2023) · Machine Learning · doi:10.1007/s10994-023-06344-7 · Cited by 51
“…incomplete supervision (Chapelle et al., 2006) (e.g., semi-supervised learning), inexact supervision (Dietterich et al., 1997; Foulds & Frank, 2010; Carbonneau et al., 2018) (e.g.,multi-instance learning) and inaccurate supervision (Frénay & Ver-leysen, 2014; Gao et al., 2016) (e.g., label noise…”
Weakly Supervised Learning Guided by Activation Mapping Applied to a Novel Citrus Pest Benchmark
Bollis, E., Pedrini, H., & Avila, S. (2020) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw50498.2020.00043 · Cited by 44
“The labels are provided only for the bags and the instances inherit from the bags creating a weakly supervised environment [4].”
Background
Developing image analysis pipelines of whole-slide images: Pre- and post-processing
Smith, B. H., Hermsen, M., Lesser, E. R., Ravichandar, D., & Kremers, W. K. (2020) · Journal of Clinical and Translational Science · doi:10.1017/cts.2020.531 · Cited by 43
“More details of these methods are provided in Carbonneau et al [33].”
BackgroundMethod
Dual Attention Networks for Few-Shot Fine-Grained Recognition
Xu, S.-L., Zhang, F., Wei, X.-S., & Wang, J. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i3.20196 · Cited by 42
“In particular, the MIL methods for image recognition (Tang et al. 2018; Carbonneau et al. 2018; Angles et al. 2021) has also achieved some good results.”
Method
A reinforced active learning approach for optimal sampling in aspect term extraction for sentiment analysis
Venugopalan, M., & Gupta, D. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.118228 · Cited by 42
A Transfer Learning-Based Multi-Instance Learning Method With Weak Labels
Xiao, Y., Liang, F., & Liu, B. (2020) · IEEE Transactions on Cybernetics · doi:10.1109/tcyb.2020.2973450 · Cited by 41
Weakly Supervised Learning for Land Cover Mapping of Satellite Image Time Series via Attention-Based CNN
Ienco, D., Gbodjo, Y. J. E., Gaetano, R., & Interdonato, R. (2020) · IEEE Access · doi:10.1109/access.2020.3024133 · Cited by 41
“Multiple Instance learning [26] (MIL) is a supervised learning paradigm in which a classiﬁcation model is learnt to supply prediction for a group of instances.”
Background
Detecting individuals with severe mental illness using artificial intelligence applied to magnetic resonance imaging
Zhang, W., Yang, C., Cao, Z., Li, Z., Zhuo, L., Tan, Y., He, Y., Yao, L., Zhou, Q. F., Gong, Q., et al. (2023) · EBioMedicine · doi:10.1016/j.ebiom.2023.104541 · Cited by 42
Chaos Based Cryptographic Pseudo-Random Number Generator Template with Dynamic State Change
Datcu, O., Macovei, C., & Hobincu, R. (2020) · Applied Sciences · doi:10.3390/app10020451 · Cited by 40
“A Monte Carlo analysis [28] with initial conditions x0, y0, z0 chosen randomly from an uniform distribution in [−2,2] was performed.”
Method
Superpixel-based multi-scale multi-instance learning for hyperspectral image classification
Huang, S., Liu, Z., Jin, W., & Mu, Y. (2024) · Pattern Recognition · doi:10.1016/j.patcog.2024.110257 · Cited by 38
Social network analysis of cell networks improves deep learning for prediction of molecular pathways and key mutations in colorectal cancer
Tajeddin, N. Z., Jahanifar, M., Bilal, M., Eastwood, M., & Rajpoot, N. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103071 · Cited by 36
Bag-Level Aggregation for Multiple-Instance Active Learning in Instance Classification Problems
Carbonneau, M., Granger, É., & Gagnon, G. (2018) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2869164 · Cited by 38
“Moreover, experiments in [13] and [22] show that single-instance classifiers often perform comparably to MIL methods, especially for instance classification.”
BackgroundMethodInfluentialSelf-citation
Listen2Cough: Leveraging End-to-End Deep Learning Cough Detection Model to Enhance Lung Health Assessment Using Passively Sensed Audio
Xu, X., Nemati, E., Vatanparvar, K., Nathan, V., Ahmed, T., Rahman, M. M., McCaffrey, D., Kuang, J., & Gao, J. A. (2021) · Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies · doi:10.1145/3448124 · Cited by 38
“Labels are only provided for entire sets, and the specific labels of the instances in the bags are not available [16].”
Background
Robust bag classification approach for multi-instance learning via subspace fuzzy clustering
Waqas, M., Tahir, M. A., & Khan, S. A. (2022) · Expert Systems with Applications · doi:10.1016/j.eswa.2022.119113 · Cited by 37
A Structure-Aware Hierarchical Graph-Based Multiple Instance Learning Framework for pT Staging in Histopathological Image
Shi, J., Tang, L., Li, Y., Zhang, X., Gao, Z., Zheng, Y., Wang, C., Gong, T., & Li, C. (2023) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2023.3273236 · Cited by 40
“WSI classification tasks [11], [12], [13], [15], [16], [17], [18], [19], [20], [21], there still exist two limitations for directly applying MIL to the challenging pT staging task.”
Automated HER2 Scoring in Breast Cancer Images Using Deep Learning and Pyramid Sampling
Selçuk, Ş. Y., Yang, X., Bai, B., Zhang, Y., Li, Y., Aydın, M., Unal, A. F., Gomatam, A., Guo, Z., Angus, D. M., et al. (2024) · BME Frontiers · doi:10.34133/bmef.0048 · Cited by 37
“MIL is a form of weakly supervised learning where training instances are arranged in sets, called bags, and a label is provided for the entire bag—usually on WSI level— instead of individual instances/image patches [25].”
A Lagrangian Relaxation Approach for Binary Multiple Instance Classification
Astorino, A., Fuduli, A., & Gaudioso, M. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2018.2885852 · Cited by 36
“Exhaustive surveys on MIL problems are [2] and [9].”
Background
A Semiproximal Support Vector Machine Approach for Binary Multiple Instance Learning
Avolio, M., & Fuduli, A. (2020) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2020.3015442 · Cited by 36
QSAR Modeling Based on Conformation Ensembles Using a Multi-Instance Learning Approach
Zankov, D. V., Matveieva, M., Nikonenko, A., Ирекович, Н. Р., Baskin, I. I., Varnek, A. A., Полищук, П. Г., & Madzhidov, T. (2021) · Journal of Chemical Information and Modeling · doi:10.1021/acs.jcim.1c00692 · Cited by 40
Weakly-supervised anomaly detection in video surveillance via graph convolutional label noise cleaning
Li, N., Zhong, J.-X., Shu, X.-J., & Guo, H. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.01.026 · Cited by 38
Classification in the multiple instance learning framework via spherical separation
Gaudioso, M., Giallombardo, G., Miglionico, G., & Vocaturo, E. (2019) · Soft Computing · doi:10.1007/s00500-019-04255-1 · Cited by 35

Authors: Carbonneau, M.-A., Cheplygina, V., Granger, E., & Gagnon, G.
Affiliations: École de Technologie Supérieure (CA)
Erasmus MC (NL)
Corresponding author: Carbonneau, Marc-André
ORCID: Carbonneau, Marc-André
Cheplygina, Veronika
Gagnon, Ghyslain
Venue: Pattern Recognition
Publisher: Elsevier BV
Published: 2018-05
Volume: 77
Pages: 329-353
ISSN: 0031-3203 (ISSN-L)
DOI: 10.1016/j.patcog.2017.10.009
OpenAlex: W2560886373
Cited by: 740
References: 223
Citations by year: 2026: 75 · 2025: 118 · 2024: 91 · 2023: 124 · 2022: 98 · 2021: 92 · 2020: 84 · 2019: 41 · 2018: 14
FWCI: 22.10
Type: journal article
Language: en
Open access: green
arxiv.org
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Pattern Recognition (publishedVersion)
arXiv (Cornell University) (submittedVersion)
TU/e Research Portal (submittedVersion)
EUR Research Repository (Erasmus University Rotterdam) (submittedVersion)
Espace ÉTS (ETS) (submittedVersion)
TU/e Research Portal (submittedVersion, other-oa)
RePub (Erasmus University Rotterdam) (submittedVersion, other-oa)
arXiv (Cornell University)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Algorithms
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
Keywords: multiple instance learning, weakly supervised learning, problem characteristics, data distribution, label ambiguity, computer vision, document classification

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