Learning with Privileged Information for Multi-Label Classification
In this paper, we propose a novel approach for learning multi-label classifiers with the help of privileged information. Specifically, we use similarity constraints to capture the relationship between available information and privileged information, and use ranking constraints to capture the dependencies among multiple labels. By integrating similarity constraints and ranking constraints into the learning process of classifiers, the privileged information and the dependencies among multiple labels are exploited to construct better classifiers during training. A maximum margin classifier is adopted, and an efficient learning algorithm of the proposed method is also developed. We evaluate the proposed method on two applications: multiple object recognition from images with the help of implicit information about object importance conveyed by the list of manually annotated image tags; and multiple facial action unit detection from low-resolution images augmented by high-resolution images. Experimental results demonstrate that the proposed method can effectively take full advantage of privileged information and dependencies among multiple labels for better object recognition and better facial action unit detection.
Code (0)
등록된 구현이 없습니다.
Tasks
Action Unit DetectionClassificationFacial Action Unit DetectionGeneral ClassificationMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONObjectObject RecognitionSimilar Papers 제목 키워드 기반
Gaussian Process Classification with Privileged Information by Soft-to-Hard Labeling Transfer
Learning using privileged information is an attractive problem setting that helps many learning scenarios in the real world. A state-of-the-art method of Gaussian process classification (GPC) with privileged information …
Gaussian ProcessesGeneral ClassificationTransfer LearningOn the Capacity Limits of Privileged ERM
We study the supervised learning paradigm called Learning Using Privileged Information, first suggested by Vapnik and Vashist (2009). In this paradigm, in addition to the examples and labels, additional (privileged) info…
Learning Using Generated Privileged Information by Text-to-Image Diffusion Models
Learning Using Privileged Information is a particular type of knowledge distillation where the teacher model benefits from an additional data representation during training, called privileged information, improving the s…
ClassificationKnowledge Distillationtext-classificationText ClassificationFairPO: Robust Preference Optimization for Fair Multi-Label Learning
We propose FairPO, a novel framework designed to promote fairness in multi-label classification by directly optimizing preference signals with a group robustness perspective. In our framework, the set of labels is partit…
FairnessMulti-Label ClassificationMUlTI-LABEL-ClASSIFICATIONMulti-Label LearningTransfer and Marginalize: Explaining Away Label Noise with Privileged Information
Supervised learning datasets often have privileged information, in the form of features which are available at training time but are not available at test time e.g. the ID of the annotator that provided the label. We arg…