Meta-Learning with label noise: A step towards label few-shot meta-learning with label noise.

Few-shot learning presents the challenging problem of learning a task with only a few provided examples. Gradient-Based Meta-Learners (GBML) offer a solution for learning such few-shot problems. These learners approach the few-shot problem by learning an initial parameterization that requires only a few adaptation steps for new tasks. Although...

Multi-AL: Robust Active learning for Multi-label Classifier

Multi-label learning is becoming more and moreimportant as real-world data often contains multi-ple labels. The dataset used for learning such aclassifier is of great importance. Acquiring a cor-rectly labelled dataset is however a difficult task.Active learning is a method which can, given anoisy dataset, identify important instances for...

Multi-Label Gold Asymmetric Loss Correction with Single-Label Regulators

Multi-label learning is an emerging extension of the multi-class classification where an image contains multiple labels. Not only acquiring a clean and fully labeled dataset in multi-label learning is extremely expensive, but also many of the actual labels are corrupted or missing due to the automated or non-expert annotation techniques. Noisy...

Web-Based Economic Activity Classification: Comparing semi-supervised text classification methods to deal with noisy labels

In order to provide accurate statistics for industries, the classification of enterprises by economic activity is an important task for national statistical institutes. The economic activity codes in the Dutch business register are less accurate for small enterprises since small enterprises are not labelled manually. To increase the quality of...

Using Publisher Partisanship for Partisan News Detection: A Comparison of Performance between Annotation Levels

News is the main source of information about events in our neighborhood and around the globe. In an era of digital news, where sources vary in quality and news spreads fast, it is critical to understand what is being consumed by the public. Partisan news is news that favors certain political parties or ideologies. It is undesirable because news...