Transductive zero-shot learning is a machine learning approach that aims to recognize unseen classes by leveraging relationships between known and unknown classes using available test data. This method goes beyond standard zero-shot learning by making predictions based on additional information from the test data, allowing the model to refine its understanding of the unseen classes. Essentially, it helps improve the performance of models when they encounter categories that were not part of the training dataset.
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