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Balanced distributed augmentation for multi-label few shot learning with prototypical network
(Institute of Electrical and Electronics Engineers Inc., 2022) Mohammed, Hamza Haruna; Öner, Alper
Many methods have been presented as a few shot learners in order to enhance few-shot learners. Some of these methods involve routine-based pre-trained language models and novel pipeline for automating the prompt generation. In this study, we propose a new evenly distributed data augmentation technique, which generates samples according to the probabilistic distribution of the relationship of each label with the mean of a label group. In the labeling phase, we present a semantic sentiment analysis approach in order to increase the realism of the data, in a more semantic augmentation way. The results show that this approach improves the few shot learners. In addition to this, we compare our adaptation approach to other traditional problem transformation methods. The newly developed approach outperforms these traditional methods, especially when the classifier learns from a limited number of samples.