Papers Weakly Supervised Classification
“Weakly Supervised Classification” 태그가 달린 논문 40편 · 필터 해제
Self-Training with Differentiable Teacher
Self-training achieves enormous success in various semi-supervised and weakly-supervised learning tasks. The method can be interpreted as a teacher-student framework, where the teacher generates pseudo-labels, and the st…
named-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)Weakly Supervised Classification+1Weakly Supervised Classification Using Group-Level Labels
In many applications, finding adequate labeled data to train predictive models is a major challenge. In this work, we propose methods to use group-level binary labels as weak supervision to train instance-level binary cl…
Binary ClassificationClassificationWeakly Supervised ClassificationWeakly-Supervised Classification and Detection of Bird Sounds in the Wild.
It is easier to hear birds than see them, however, they still play an essential role in nature and they are excellent indicators of deteriorating environmental quality and pollution. Recent advances in Machine Learning a…
Audio ClassificationAudio TaggingBird Audio DetectionSound Event Detection+1On the Robustness of Average Losses for Partial-Label Learning
Partial-label learning (PLL) utilizes instances with PLs, where a PL includes several candidate labels but only one is the true label (TL). In PLL, identification-based strategy (IBS) purifies each PL on the fly to selec…
Partial Label LearningWeakly Supervised ClassificationTransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification
Multiple instance learning (MIL) is a powerful tool to solve the weakly supervised classification in whole slide image (WSI) based pathology diagnosis. However, the current MIL methods are usually based on independent an…
Classificationimage-classificationImage ClassificationMultiple Instance Learning+1Knodle: Modular Weakly Supervised Learning with PyTorch
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. In this wo…
BenchmarkingBIG-bench Machine LearningRelation ExtractionSentiment Analysis+3Lower-Bounded Proper Losses for Weakly Supervised Classification
This paper discusses the problem of weakly supervised classification, in which instances are given weak labels that are produced by some label-corruption process. The goal is to derive conditions under which loss functio…
ClassificationGeneral ClassificationWeakly Supervised ClassificationLearning Noise Transition Matrix from Only Noisy Labels via Total Variation Regularization
Many weakly supervised classification methods employ a noise transition matrix to capture the class-conditional label corruption. To estimate the transition matrix from noisy data, existing methods often need to estimate…
Weakly Supervised ClassificationInteractive Weak Supervision: Learning Useful Heuristics for Data Labeling
Obtaining large annotated datasets is critical for training successful machine learning models and it is often a bottleneck in practice. Weak supervision offers a promising alternative for producing labeled datasets with…
Weakly Supervised ClassificationHistorical Credibility for Movie Reviews and Its Application to Weakly Supervised Classification
In this study, we deal with the problem of judging the credibility of movie reviews. The problem is challenging because even experts cannot clearly and efficiently judge the credibility of a movie review and the number o…
ClassificationWeakly Supervised ClassificationOntology-driven weak supervision for clinical entity classification in electronic health records
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. However, creati…
General ClassificationNamed Entity Recognition (NER)Temporal Information ExtractionWeakly Supervised Classification+1Data Efficient and Weakly Supervised Computational Pathology on Whole Slide Images
The rapidly emerging field of computational pathology has the potential to enable objective diagnosis, therapeutic response prediction and identification of new morphological features of clinical relevance. However, deep…
ClusteringDiagnosticDomain AdaptationMultiple Instance Learning+2Meta-learning for robust child-adult classification from speech
Computational modeling of naturalistic conversations in clinical applications has seen growing interest in the past decade. An important use-case involves child-adult interactions within the autism diagnosis and interven…
ClassificationMeta-Learningspeaker-diarizationSpeaker Diarization+1Discriminative Topic Mining via Category-Name Guided Text Embedding
Mining a set of meaningful and distinctive topics automatically from massive text corpora has broad applications. Existing topic models, however, typically work in a purely unsupervised way, which often generate topics t…
Document ClassificationGeneral ClassificationLexical EntailmentTopic Models+1Effective weakly supervised semantic frame induction using expression sharing in hierarchical hidden Markov models
We present a framework for the induction of semantic frames from utterances in the context of an adaptive command-and-control interface. The system is trained on an individual user's utterances and the corresponding sema…
Weakly Supervised ClassificationAttention-Guided Curriculum Learning for Weakly Supervised Classification and Localization of Thoracic Diseases on Chest Radiographs
In this work, we exploit the task of joint classification and weakly supervised localization of thoracic diseases from chest radiographs, with only image-level disease labels coupled with disease severity-level (DSL) inf…
ClassificationGeneral ClassificationWeakly Supervised ClassificationInformation-Theoretic Representation Learning for Positive-Unlabeled Classification
Recent advances in weakly supervised classification allow us to train a classifier only from positive and unlabeled (PU) data. However, existing PU classification methods typically require an accurate estimate of the cla…
ClassificationDimensionality ReductionGeneral ClassificationRepresentation Learning+1ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
The chest X-ray is one of the most commonly accessible radiological examinations for screening and diagnosis of many lung diseases. A tremendous number of X-ray imaging studies accompanied by radiological reports are acc…
General Classificationimage-classificationImage ClassificationLung Disease Classification+3Weakly Supervised Classification in High Energy Physics
As machine learning algorithms become increasingly sophisticated to exploit subtle features of the data, they often become more dependent on simulations. This paper presents a new approach called weakly supervised classi…
BIG-bench Machine LearningBinary ClassificationClassificationGeneral Classification+2A Weakly Supervised Learning Approach based on Spectral Graph-Theoretic Grouping
In this study, a spectral graph-theoretic grouping strategy for weakly supervised classification is introduced, where a limited number of labelled samples and a larger set of unlabelled samples are used to construct a la…
ClassificationGeneral ClassificationWeakly Supervised ClassificationWeakly-supervised Learning