Missing Labels
41 papers with code • 0 benchmarks • 0 datasets
The challenge in multi-label learning with missing labels is that the training data often has incomplete label information. Collecting labels for multi-label datasets is a manual exercise and dependent on external sources, leading to the collection of only a subset of labels. This assumption of complete label information doesn't hold, especially when the label space is large. Inaccurate label-label and label-feature relationships can be captured, leading to suboptimal solutions in missing label settings.
Benchmarks
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Most implemented papers
Semi-Supervised Online Structure Learning for Composite Event Recognition
Online structure learning approaches, such as those stemming from Statistical Relational Learning, enable the discovery of complex relations in noisy data streams.
Visual Object Tracking: The Initialisation Problem
This BB may contain a large number of background pixels in addition to the object and can lead to parts-based tracking algorithms initialising their object models in background regions of the BB.
Fairness Under Unawareness: Assessing Disparity When Protected Class Is Unobserved
We also propose an alternative weighted estimator that uses soft classification, and show that its bias arises simply from the conditional covariance of the outcome with the true class membership.
A Flexible Generative Framework for Graph-based Semi-supervised Learning
In this work, we propose a flexible generative framework for graph-based semi-supervised learning, which approaches the joint distribution of the node features, labels, and the graph structure.
Unblind Your Apps: Predicting Natural-Language Labels for Mobile GUI Components by Deep Learning
However, the prerequisite of using screen readers is that developers have to add natural-language labels to the image-based components when they are developing the app.
A Concise yet Effective model for Non-Aligned Incomplete Multi-view and Missing Multi-label Learning
In aligning, we characterize the global and local structures of multiple labels to be high-rank and low-rank, respectively.
openXDATA: A Tool for Multi-Target Data Generation and Missing Label Completion
A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels.
Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling
In step II, we augment the initial imputations to ensure the consistency of the resulting estimators regardless of the specification of the prediction model or the imputation model.
Graph Stochastic Neural Networks for Semi-supervised Learning
Graph Neural Networks (GNNs) have achieved remarkable performance in the task of the semi-supervised node classification.
Recovering the Unbiased Scene Graphs from the Biased Ones
coldmanck/recovering-unbiased-scene-graphs
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Given input images, scene graph generation (SGG) aims to produce comprehensive, graphical representations describing visual relationships among salient objects.
