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View all- Liang CTian YZhao DLi MPan SZhang HWei J(2024)Bootstrap Latent Prototypes for graph positive-unlabeled learningInformation Fusion10.1016/j.inffus.2024.102553112(102553)Online publication date: Dec-2024
In the multi-instance learning (MIL) setting instances are grouped together into bags. Labels are provided only for the bags and not on the level of individual instances. A positive bag label means that at least one instance inside the bag is positive, ...
Positive and Unlabeled (PU) learning aims to learn a binary classifier from only positive and unlabeled training data. The state-of-the-art methods usually formulate PU learning as a cost-sensitive learning problem, in which every unlabeled example is ...
Few-shot learning has made significant progress recently thanks to pre-training methods and meta-learning approaches. These methods, however, require an extensive labeled dataset that is difficult to obtain. We propose an unsupervised few-shot ...
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