Weakly supervised clustering: Learning fine-grained signals from coarse labels
Consider a classification problem where we do not have access to labels for individual training examples, but only have average labels over subpopulations. We give practical examples of this setup and show how such a classification task can usefully be analyzed as a weakly supervised clustering problem. We propose three approaches to solving the weakly supervised clustering problem, including a latent variables model that performs well in our experiments. We illustrate our methods on an analysis of aggregated elections data and an industry data set that was the original motivation for this research.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationClusteringGeneral ClassificationSimilar Papers 제목 키워드 기반
Weakly-Supervised Temporal Action Detection for Fine-Grained Videos with Hierarchical Atomic Actions
Action understanding has evolved into the era of fine granularity, as most human behaviors in real life have only minor differences. To detect these fine-grained actions accurately in a label-efficient way, we tackle the…
Action DetectionAction UnderstandingFine-Grained Action DetectionWeakly Supervised Action LocalizationContrastive Prompt Clustering for Weakly Supervised Semantic Segmentation
Weakly Supervised Semantic Segmentation (WSSS) with image-level labels has gained attention for its cost-effectiveness. Most existing methods emphasize inter-class separation, often neglecting the shared semantics among …
Semantic SegmentationWeakly-Supervised Learning of Visual Relations in Multimodal Pretraining
Recent work in vision-and-language pretraining has investigated supervised signals from object detection data to learn better, fine-grained multimodal representations. In this work, we take a step further and explore how…
object-detectionObject DetectionRelationRelation Prediction+1Weakly Supervised Multi-Label Classification of Full-Text Scientific Papers
Instead of relying on human-annotated training samples to build a classifier, weakly supervised scientific paper classification aims to classify papers only using category descriptions (e.g., category names, category-ind…
Multi-Label ClassificationMUlTI-LABEL-ClASSIFICATIONDistribution Guidance Network for Weakly Supervised Point Cloud Semantic Segmentation
Despite alleviating the dependence on dense annotations inherent to fully supervised methods, weakly supervised point cloud semantic segmentation suffers from inadequate supervision signals. In response to this challenge…
Semantic SegmentationWeakly-supervised Learning