Everything About Multiple Instance Learning

Multiple instance learning papers, codes, and libraries

• *Inspired by awesome-machine-learning.

• Frameworks and libraries are grouped by programming language.

• Research papers are grouped by research field.

• There are numerous papers in this field of research, so this list is not intended to be exhaustive.

Table of Contents

1. Frameworks and Libraries

1.1 Python

• [Multiple Instance Active Learning for Object Detection][Github][Paper]

NOTE: written in python, easy to use.

• [DSMIL: Dual-stream multiple instance learning networks for tumor detection in Whole Slide Image][Github][Paper]

NOTE: written in python, easy to use.

• [mil: multiple instance learning library for Python][Github]

NOTE: written in python, easy to use.

1.2 R

• smote_variants [Documentation][Github] - A collection of 85 minority over-sampling techniques for imbalanced learning with multi-class oversampling and model selection features (All writen in Python, also support R and Julia).

• caret [Documentation][Github] - Contains the implementation of Random under/over-sampling.

• ROSE [Documentation] - Contains the implementation of ROSE (Random Over-Sampling Examples).

• DMwR [Documentation] - Contains the implementation of SMOTE (Synthetic Minority Over-sampling Technique).

1.3 Java

• KEEL [Github][Paper] - KEEL provides a simple GUI based on data flow to design experiments with different datasets and computational intelligence algorithms (paying special attention to evolutionary algorithms) in order to assess the behavior of the algorithms. This tool includes many widely used imbalanced learning techniques such as (evolutionary) over/under-resampling, cost-sensitive learning, algorithm modification, and ensemble learning methods.

NOTE: wide variety of classical classification, regression, preprocessing algorithms included.

1.4 Scalar

• undersampling [Documentation][Github] - A Scala library for under-sampling and their ensemble variants in imbalanced classification.

1.5 Julia

• smote_variants [Documentation][Github] - A collection of 85 minority over-sampling techniques for imbalanced learning with multi-class oversampling and model selection features (All writen in Python, also support R and Julia).

2. Research Papers

2.1 Surveys

• Learning from imbalanced data (IEEE TKDE, 2009, 6000+ citations) [Paper]
• Highly cited, classic survey paper. It systematically reviewed the popular solutions, evaluation metrics, and challenging problems in future research in this area (as of 2009).

• Learning from imbalanced data: open challenges and future directions (2016, 900+ citations) [Paper]
• This paper concentrates on the open issues and challenges in imbalanced learning, i.e., extreme class imbalance, imbalance in online/stream learning, multi-class imbalanced learning, and semi/un-supervised imbalanced learning.

• Learning from class-imbalanced data: Review of methods and applications (2017, 900+ citations) [Paper]
• A recent exhaustive survey of imbalanced learning methods and applications, a total of 527 papers were included in this study. It provides several detailed taxonomies of existing methods and also the recent trend of this research area.

2.2 Ensemble Learning

2.2.1 General ensemble

• Self-paced Ensemble (ICDE 2020, 20+ citations) [Paper][Code][Slides][Zhihu/知乎][PyPI]

NOTE: versatile solution with outstanding performance and computational efficiency.

• MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler (NeurIPS 2020) [Paper][Code][Video][Zhihu/知乎]

NOTE: learning an optimal sampling policy directly from data.

• Exploratory Undersampling for Class-Imbalance Learning (IEEE Trans. on SMC, 2008, 1300+ citations) [Paper]

NOTE: simple but effective solution.

• EasyEnsemble [Code]
• BalanceCascade [Code]

2.2.2 Boosting-based

• AdaBoost (1995, 18700+ citations) [Paper][Code] - Adaptive Boosting with C4.5

• DataBoost (2004, 570+ citations) [Paper] - Boosting with Data Generation for Imbalanced Data

• SMOTEBoost (2003, 1100+ citations) [Paper][Code] - Synthetic Minority Over-sampling TEchnique Boosting

• MSMOTEBoost (2011, 1300+ citations) [Paper] - Modified Synthetic Minority Over-sampling TEchnique Boosting

• RAMOBoost (2010, 140+ citations) [Paper] [Code] - Ranked Minority Over-sampling in Boosting

• RUSBoost (2009, 850+ citations) [Paper] [Code] - Random Under-Sampling Boosting

• AdaBoostNC (2012, 350+ citations) [Paper] - Adaptive Boosting with Negative Correlation Learning

• EUSBoost (2013, 210+ citations) [Paper] - Evolutionary Under-sampling in Boosting

2.2.3 Bagging-based

• Bagging (1996, 20000+ citations) [Paper][Code] - Bagging predictor

• Diversity Analysis on Imbalanced Data Sets by Using Ensemble Models (2009, 400+ citations) [Paper]
• UnderBagging [Code]
• OverBagging [Code]
• SMOTEBagging [Code]

2.2.4 Cost-sensitive ensemble

• AdaCost (ICML 1999, 800+ citations) [Paper][Code] - Misclassification Cost-sensitive boosting

• AdaUBoost (NIPS 1999, 100+ citations) [Paper][Code] - AdaBoost with Unequal loss functions

• AsymBoost (NIPS 2001, 700+ citations) [Paper][Code] - Asymmetric AdaBoost and detector cascade

2.3 Data resampling

2.3.1 Over-sampling

• ROS [Code] - Random Over-sampling

• SMOTE (2002, 9800+ citations) [Paper][Code] - Synthetic Minority Over-sampling TEchnique

• Borderline-SMOTE (2005, 1400+ citations) [Paper][Code] - Borderline-Synthetic Minority Over-sampling TEchnique

• ADASYN (2008, 1100+ citations) [Paper][Code] - ADAptive SYNthetic Sampling

• SPIDER (2008, 150+ citations) [Paper][Code(Java)] - Selective Preprocessing of Imbalanced Data

• Safe-Level-SMOTE (2009, 370+ citations) [Paper][Code(Java)] - Safe Level Synthetic Minority Over-sampling TEchnique

• SVM-SMOTE (2009, 120+ citations) [Paper][Code] - SMOTE based on Support Vectors of SVM

• MDO (2015, 150+ citations) [Paper][Code] - Mahalanobis Distance-based Over-sampling for Multi-Class imbalanced problems.

NOTE: See more over-sampling methods at smote-variants.

2.3.2 Under-sampling

• RUS [Code] - Random Under-sampling

• CNN (1968, 2100+ citations) [Paper][Code] - Condensed Nearest Neighbor

• ENN (1972, 1500+ citations) [Paper] [Code] - Edited Condensed Nearest Neighbor

• TomekLink (1976, 870+ citations) [Paper][Code] - Tomek’s modification of Condensed Nearest Neighbor

• NCR (2001, 500+ citations) [Paper][Code] - Neighborhood Cleaning Rule

• NearMiss-1 & 2 & 3 (2003, 420+ citations) [Paper][Code] - Several kNN approaches to unbalanced data distributions.

• CNN with TomekLink (2004, 2000+ citations) [Paper][Code(Java)] - Condensed Nearest Neighbor + TomekLink

• OSS (2007, 2100+ citations) [Paper][Code] - One Side Selection

• EUS (2009, 290+ citations) [Paper] - Evolutionary Under-sampling

• IHT (2014, 130+ citations) [Paper][Code] - Instance Hardness Threshold

2.3.3 Hybrid-sampling

• A Study of the Behavior of Several Methods for Balancing Training Data (2004, 2000+ citations) [Paper]

NOTE: extensive experimental evaluation involving 10 different over/under-sampling methods.

• SMOTE-Tomek [Code]
• SMOTE-ENN [Code]

• SMOTE-RSB (2012, 210+ citations) [Paper][Code] - Hybrid Preprocessing using SMOTE and Rough Sets Theory

• SMOTE-IPF (2015, 180+ citations) [Paper][Code] - SMOTE with Iterative-Partitioning Filter

2.4 Cost-sensitive Learning

• CSC4.5 (2002, 420+ citations) [Paper][Code(Java)] - An instance-weighting method to induce cost-sensitive trees

• CSSVM (2008, 710+ citations) [Paper][Code(Java)] - Cost-sensitive SVMs for highly imbalanced classification

• CSNN (2005, 950+ citations) [Paper][Code(Java)] - Training cost-sensitive neural networks with methods addressing the class imbalance problem.

2.5 Deep Learning

2.5.1 Surveys

• A systematic study of the class imbalance problem in convolutional neural networks (2018, 330+ citations) [Paper]

• Survey on deep learning with class imbalance (2019, 50+ citations) [Paper]

NOTE: a recent comprehensive survey of the class imbalance problem in deep learning.

2.5.2 Graph Data Mining

• Semi-Supervised Graph Imbalanced Regression (KDD 2023) [Paper] [Code]

• TAM: Topology-Aware Margin Loss for Class-Imbalanced Node Classification (ICML 2022) [Paper][Code]

• GraphSMOTE: Imbalanced Node Classification on Graphs with Graph Neural Networks (WSDM 2021) [Paper][Code]

• Topology-Imbalance Learning for Semi-Supervised Node Classification (NeurIPS 2021) [Paper][Code]

• GraphENS: Neighbor-Aware Ego Network Synthesis for Class-Imbalanced Node Classification (ICLR 2022) [Paper][Code]

• LTE4G: Long-Tail Experts for Graph Neural Networks (CIKM 2022) [Paper][Code]

• Multi-Class Imbalanced Graph Convolutional Network Learning (IJCAI 2020) [Paper]

2.5.3 Hard example mining

• Training region-based object detectors with online hard example mining (CVPR 2016, 840+ citations) [Paper][Code] - In the later phase of NN training, only do gradient back-propagation for “hard examples” (i.e., with large loss value)

2.5.4 Loss function engineering

• Focal loss for dense object detection (ICCV 2017, 2600+ citations) [Paper][Code (detectron2)][Code (unofficial)] - A uniform loss function that focuses training on a sparse set of hard examples to prevents the vast number of easy negatives from overwhelming the detector during training.

NOTE: elegant solution, high influence.

• Training deep neural networks on imbalanced data sets (IJCNN 2016, 110+ citations) [Paper] - Mean (square) false error that can equally capture classification errors from both the majority class and the minority class.

• Deep imbalanced attribute classification using visual attention aggregation (ECCV 2018, 30+ citation) [Paper][Code]

• Imbalanced deep learning by minority class incremental rectification (TPAMI 2018, 60+ citations) [Paper] - Class Rectification Loss for minimizing the dominant effect of majority classes by discovering sparsely sampled boundaries of minority classes in an iterative batch-wise learning process.

• Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss (NIPS 2019, 10+ citations) [Paper][Code] - A theoretically-principled label-distribution-aware margin (LDAM) loss motivated by minimizing a margin-based generalization bound.

• Gradient harmonized single-stage detector (AAAI 2019, 40+ citations) [Paper][Code] - Compared to Focal Loss, which only down-weights “easy” negative examples, GHM also down-weights “very hard” examples as they are likely to be outliers.

• Class-Balanced Loss Based on Effective Number of Samples (CVPR 2019, 70+ citations) [Paper][Code] - a simple and generic class-reweighting mechanism based on Effective Number of Samples.

• Influence-Balanced Loss for Imbalanced Visual Classification (ICCV 2021) [Paper][Code]

• AutoBalance: Optimized Loss Functions for Imbalanced Data (NeurIPS 2021) [Paper]

• Label-Imbalanced and Group-Sensitive Classification under Overparameterization (NeurIPS 2021) [Paper][Code]

2.5.5 Meta-learning

• Learning to model the tail (NIPS 2017, 70+ citations) [Paper] - Transfer meta-knowledge from the data-rich classes in the head of the distribution to the data-poor classes in the tail.

• Learning to reweight examples for robust deep learning (ICML 2018, 150+ citations) [Paper][Code] - Implicitly learn a weight function to reweight the samples in gradient updates of DNN.

NOTE: representative work to solve the class imbalance problem through meta-learning.

• Meta-weight-net: Learning an explicit mapping for sample weighting (NIPS 2019) [Paper][Code] - Explicitly learn a weight function (with an MLP as the function approximator) to reweight the samples in gradient updates of DNN.

• Learning Data Manipulation for Augmentation and Weighting (NIPS 2019) [Paper][Code]

• Learning to Balance: Bayesian Meta-Learning for Imbalanced and Out-of-distribution Tasks (ICLR 2020) [Paper][Code]

• MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler (NeurIPS 2020) [Paper][Code][Video]

NOTE: meta-learning-powered ensemble learning

2.5.6 Representation Learning

• Learning deep representation for imbalanced classification (CVPR 2016, 220+ citations) [Paper]

• Supervised Class Distribution Learning for GANs-Based Imbalanced Classification (ICDM 2019) [Paper]

• Decoupling Representation and Classifier for Long-tailed Recognition (ICLR 2020) [Paper][Code]

NOTE: interesting findings on representation learning and classifier learning

• Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer (NeurIPS 2021) [Paper]

• Tailoring Self-Supervision for Supervised Learning (ECCV 2022) [Paper][Code]

2.5.7 Posterior Recalibration

• Posterior Re-calibration for Imbalanced Datasets (NeurIPS 2020) [Paper][Code]

• Long-tail learning via logit adjustment (ICLR 2021) [Paper][Code]

2.5.8 Semi/Self-supervised Learning

• Rethinking the Value of Labels for Improving Class-Imbalanced Learning (NeurIPS 2020) [Paper][Code][Video]

NOTE: semi-supervised training / self-supervised pre-training helps imbalance learning

• Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning (NeurIPS 2020) [Paper][Code]

• ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning (NeurIPS 2021) [Paper][Code]

• Improving Contrastive Learning on Imbalanced Data via Open-World Sampling (NeurIPS 2021) [Paper]

• DASO: Distribution-Aware Semantics-Oriented Pseudo-label for Imbalanced Semi-Supervised Learning (CVPR 2022) [Paper][Code]

2.5.9 Curriculum Learning

• Dynamic Curriculum Learning for Imbalanced Data Classification (ICCV 2019) [Paper]

2.5.10 Two-phase Training

• Brain tumor segmentation with deep neural networks (2017, 1200+ citations) [Paper][Code (unofficial)]

Pre-training on balanced dataset, fine-tuning the last output layer before softmax on the original, imbalanced data.

2.5.11 Network Architecture

• BBN: Bilateral-Branch Network with Cumulative Learning for Long-Tailed Visual Recognition (CVPR 2020) [Paper][Code]

• Class-Imbalanced Deep Learning via a Class-Balanced Ensemble (TNNLS 2021) [Paper]

2.5.12 Deep Generative Model

• Deep Generative Model for Robust Imbalance Classification (CVPR 2020) [Paper]

2.5.13 Imbalanced Regression

• Semi-Supervised Graph Imbalanced Regression (KDD 2023) [Paper] [Code]

• RankSim: Ranking Similarity Regularization for Deep Imbalanced Regression (ICML 2022) [Paper] [Code]

• Balanced MSE for Imbalanced Visual Regression (CVPR 2022) [Paper] [Code]

• Delving into Deep Imbalanced Regression (ICML 2021) [Paper][Code][Video]

• Density-based weighting for imbalanced regression (Machine Learning [J], 2021) [Paper][Code]

2.5.14 Data Augmentation

• Minority-Oriented Vicinity Expansion with Attentive Aggregation for Video Long-Tailed Recognition (AAAI 2023) [Paper][Code]

3. Miscellaneous

3.1 Datasets

• Common MIL datasets

Application areas of the datasets are molecular activity prediction, image annotation, text categorization, webpage classification and audio-recording classification Each dataset file is a comma-separated value (CSV) formatted file which has number of instances many rows and number of features many columns together with two additionally attached columns. The first attached column corresponds to the bag class labels which are propagated to the instances. The second column is the bag ID column where each instance receives the bag ID number of its owner bag. The remaining columns individually store the feature values of the instances.

• Full table of the datasets: MIL_datasets

• Link: https://www.dropbox.com/home/Real_world_datasets

Cross Validation Indices

3.2 Results

• 10 fold cross-validation is repeated 5 times.

3.3 Codes