Poster
Co-Representation Network for Generalized Zero-Shot Learning
Fei Zhang · Guangming Shi
Pacific Ballroom #144
Keywords: [ Architectures ] [ Computer Vision ] [ Representation Learning ] [ Semi-supervised learning ] [ Transfer and Multitask Learning ]
Generalized zero-shot learning is a significant topic but faced with bias problem, which leads to unseen classes being easily misclassified into seen classes. Hence we propose a embedding model called co-representation network to learn a more uniform visual embedding space that effectively alleviates the bias problem and helps with classification. We mathematically analyze our model and find it learns a projection with high local linearity, which is proved to cause less bias problem. The network consists of a cooperation module for representation and a relation module for classification, it is simple in structure and can be easily trained in an end-to-end manner. Experiments show that our method outperforms existing generalized zero-shot learning methods on several benchmark datasets.
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