Download or read book Learning from Hierarchical and Noisy Labels written by Wenting Qi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: One branch of machine learning algorithms is supervised learning, where the label is crucial for the learning model. Numerous algorithms have been proposed for supervised learning with different classification tasks. However, fewer works question the quality of the training labels. Training a learning model with noisy labels leads to decreased or untruthful performance. On the other hand, hierarchical multi–label classification (HMC) is one of the most challenging problems in machine learning because the classes in HMC tasks are hierarchically structured, and data instances are associated with multiple labels residing in a path of the hierarchy. Treating hierarchical tasks as flat and ignoring the hierarchical relationship between labels can degrade the model’s performance. Therefore, in this thesis, we focus on learning from two types of difficult labels: noisy labels and hierarchical labels.