SOTAVerified

Multiple Instance Learning

Multiple Instance Learning is a type of weakly supervised learning algorithm where training data is arranged in bags, where each bag contains a set of instances $X=\{x_1,x_2, \ldots,x_M\}$, and there is one single label $Y$ per bag, $Y\in\{0, 1\}$ in the case of a binary classification problem. It is assumed that individual labels $y_1, y_2,\ldots, y_M$ exist for the instances within a bag, but they are unknown during training. In the standard Multiple Instance assumption, a bag is considered negative if all its instances are negative. On the other hand, a bag is positive, if at least one instance in the bag is positive.

Source: Monte-Carlo Sampling applied to Multiple Instance Learning for Histological Image Classification

Papers

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Showing 651–700 of 744 papers

TitleStatusHype
Multiple Instance Hybrid Estimator for Hyperspectral Target Characterization and Sub-pixel Target Detection—0
Progressive Representation Adaptation for Weakly Supervised Object LocalizationCode0
Explaining Aviation Safety Incidents Using Deep Temporal Multiple Instance Learning—0
Bag-Level Aggregation for Multiple Instance Active Learning in Instance Classification Problems—0
An In-field Automatic Wheat Disease Diagnosis System—0
An End-to-End Deep Framework for Answer Triggering with a Novel Group-Level Objective—0
NITE: A Neural Inductive Teaching Framework for Domain Specific NER—0
Variational Bayesian Multiple Instance Learning With Gaussian ProcessesCode0
Efficient Multiple Instance Metric Learning Using Weakly Supervised Data—0
Video Segmentation via Multiple Granularity Analysis—0
Multiple Instance Dictionary Learning for Beat-to-Beat Heart Rate Monitoring from Ballistocardiograms—0
Automatic Emphysema Detection using Weakly Labeled HRCT Lung Images—0
Ensemble of Part Detectors for Simultaneous Classification and Localization—0
Deep Patch Learning for Weakly Supervised Object Classification and DiscoveryCode0
Convex Formulation of Multiple Instance Learning from Positive and Unlabeled BagsCode0
Training object class detectors with click supervision—0
Action Representation Using Classifier Decision Boundaries—0
Classification of Diabetic Retinopathy Images Using Multi-Class Multiple-Instance Learning Based on Color Correlogram Features—0
Multiple Instance Learning with the Optimal Sub-Pattern Assignment Metric—0
Weakly Supervised Object Localization Using Things and Stuff Transfer—0
Label Stability in Multiple Instance Learning—0
Classification of COPD with Multiple Instance Learning—0
Model-Based Multiple Instance Learning—0
Explicit Document Modeling through Weighted Multiple-Instance LearningCode0
Multiple Instance Hybrid Estimator for Learning Target Signatures—0
Constrained Deep Weak Supervision for Histopathology Image Segmentation—0
PIGMIL: Positive Instance Detection via Graph Updating for Multiple Instance Learning—0
Multiple Instance Learning: A Survey of Problem Characteristics and ApplicationsCode0
User Personalized Satisfaction Prediction via Multiple Instance Deep Learning—0
Weakly Supervised Cascaded Convolutional Networks—0
Learning Time Series Detection Models from Temporally Imprecise Labels—0
Human versus Machine Attention in Document Classification: A Dataset with Crowdsourced Annotations—0
Multiple Instance Fuzzy Inference Neural Networks—0
Multiple Instance Learning Convolutional Neural Networks for Object Recognition—0
Revisiting Multiple Instance Neural Networks—0
Discriminatively Trained Latent Ordinal Model for Video Classification—0
Modeling Context Between Objects for Referring Expression UnderstandingCode0
Weakly Supervised Scalable Audio Content Analysis—0
WELDON: Weakly Supervised Learning of Deep Convolutional Neural NetworksCode0
Weakly Supervised Object Localization With Progressive Domain Adaptation—0
Self Paced Deep Learning for Weakly Supervised Object DetectionCode0
Heart Beat Characterization from Ballistocardiogram Signals using Extended Functions of Multiple Instances—0
Audio Event Detection using Weakly Labeled Data—0
Detection of Major ASL Sign Types in Continuous Signing For ASL Recognition—0
Spot On: Action Localization from Pointly-Supervised Proposals—0
Learning Models for Actions and Person-Object Interactions with Transfer to Question Answering—0
LOMo: Latent Ordinal Model for Facial Analysis in Videos—0
Instance Influence Estimation for Hyperspectral Target Signature Characterization using Extended Functions of Multiple Instances—0
On the Complexity of One-class SVM for Multiple Instance Learning—0
Multiple instance learning for sequence data with across bag dependencies—0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1Snuffy (DINO Exhaustive)AUC0.99—Unverified
2Snuffy (SimCLR Exhaustive)AUC0.97—Unverified
3CAMILAUC0.96—Unverified
4CAMIL (CAMIL-L)AUC0.95—Unverified
5CAMIL (CAMIL-G)AUC0.95—Unverified
6DTFD-MIL (AFS)AUC0.95—Unverified
7DTFD-MIL (MAS)AUC0.95—Unverified
8DTFD-MIL (MaxMinS)AUC0.94—Unverified
9TransMILAUC0.93—Unverified
10DSMIL-LCAUC0.92—Unverified
#ModelMetricClaimedVerifiedStatus
1DTFD-MIL (MAS)AUC0.96—Unverified
2DTFD-MIL (AFS)ACC0.95—Unverified
3Snuffy (SimCLR Exhaustive)ACC0.95—Unverified
4DSMIL-LCACC0.93—Unverified
5DSMILACC0.92—Unverified
6DTFD-MIL (MaxMinS)ACC0.89—Unverified
7TransMILACC0.88—Unverified
8DTFD-MIL (MaxS)ACC0.87—Unverified
#ModelMetricClaimedVerifiedStatus
1SnuffyAUC0.97—Unverified
2DSMILACC0.93—Unverified
#ModelMetricClaimedVerifiedStatus
1SnuffyACC0.96—Unverified
2DSMILACC0.95—Unverified
#ModelMetricClaimedVerifiedStatus
1DSMILACC0.93—Unverified
2SnuffyACC0.79—Unverified