Weakly Supervised Classification

20 papers with code • 2 benchmarks • 4 datasets

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Most implemented papers

ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases

arnoweng/CheXNet CVPR 2017

The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases.

TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification

szc19990412/TransMIL NeurIPS 2021

Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis.

Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models

clips/patcor 30 Jan 2019

We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface.

Discriminative Topic Mining via Category-Name Guided Text Embedding

yumeng5/CatE 20 Aug 2019

We propose a new task, discriminative topic mining, which leverages a set of user-provided category names to mine discriminative topics from text corpora.

Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images

mahmoodlab/CLAM 20 Apr 2020

CLAM is a general-purpose and adaptable method that can be used for a variety of different computational pathology tasks in both clinical and research settings.

Ontology-driven weak supervision for clinical entity classification in electronic health records

som-shahlab/trove 5 Aug 2020

In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e. g. the order of an event relative to a time index) can inform many important analyses.

Interactive Weak Supervision: Learning Useful Heuristics for Data Labeling

benbo/interactive-weak-supervision ICLR 2021

Our experiments demonstrate that only a small number of feedback iterations are needed to train models that achieve highly competitive test set performance without access to ground truth training labels.

Learning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization

YivanZhang/lio 4 Feb 2021

To estimate the transition matrix from noisy data, existing methods often need to estimate the noisy class-posterior, which could be unreliable due to the overconfidence of neural networks.

Lower-Bounded Proper Losses for Weakly Supervised Classification

yoshum/lower-bounded-proper-losses 4 Mar 2021

To this end, we derive a representation theorem for proper losses in supervised learning, which dualizes the Savage representation.

Knodle: Modular Weakly Supervised Learning with PyTorch

knodle/knodle ACL (RepL4NLP) 2021

Strategies for improving the training and prediction quality of weakly supervised machine learning models vary in how much they are tailored to a specific task or integrated with a specific model architecture.