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Methodology
← Learning Types
Machine Learning
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Learning Types
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Weakly Supervised Learning
3895 directly classified papers
Papers per year
2002: 5
2003: 3
2004: 3
2005: 1
2006: 7
2007: 8
2008: 7
2009: 13
2010: 20
2011: 7
2012: 11
2013: 43
2014: 35
2015: 66
2016: 74
2017: 133
2018: 194
2019: 388
2020: 388
2021: 566
2022: 469
2023: 588
2024: 435
2025: 350
2026: 81
Papers
Relaxed Multiple-Instance SVM With Application to Object Discovery
ICCV 2015
A Framework for Individualizing Predictions of Disease Trajectories by Exploiting Multi-Resolution Structure
NIPS 2015
Semantic Object Segmentation via Detection in Weakly Labeled Video
CVPR 2015
Transferring a Semantic Representation for Person Re-Identification and Search
CVPR 2015
Simpler Non-Parametric Methods Provide as Good or Better Results to Multiple-Instance Learning
ICCV 2015
Learning with Relaxed Supervision
NIPS 2015
Learning to Segment Under Various Forms of Weak Supervision
CVPR 2015
From Image-Level to Pixel-Level Labeling With Convolutional Networks
CVPR 2015
Video Segmentation With Just a Few Strokes
ICCV 2015
Discriminative and Consistent Similarities in Instance-Level Multiple Instance Learning
CVPR 2015
Learning Using Privileged Information: Similarity Control and Knowledge Transfer
JMLR 2015
Discovering the Spatial Extent of Relative Attributes
ICCV 2015
Weakly Supervised Graph Based Semantic Segmentation by Learning Communities of Image-Parts
ICCV 2015
Constrained Convolutional Neural Networks for Weakly Supervised Segmentation
ICCV 2015
Active Learning from Weak and Strong Labelers
NIPS 2015
The Self-Normalized Estimator for Counterfactual Learning
NIPS 2015
Weakly-Supervised Structured Output Learning With Flexible and Latent Graphs Using High-Order Loss Functions
ICCV 2015
Predtron: A Family of Online Algorithms for General Prediction Problems
NIPS 2015
Joint Multi-Feature Spatial Context for Scene Recognition on the Semantic Manifold
CVPR 2015
ConceptLearner: Discovering Visual Concepts From Weakly Labeled Image Collections
CVPR 2015
Weakly Supervised Object Detection With Convex Clustering
CVPR 2015
Learning From Massive Noisy Labeled Data for Image Classification
CVPR 2015
Max-Margin Majority Voting for Learning from Crowds
NIPS 2015
Is Object Localization for Free? - Weakly-Supervised Learning With Convolutional Neural Networks
CVPR 2015
The Application of Two-Level Attention Models in Deep Convolutional Neural Network for Fine-Grained Image Classification
CVPR 2015
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