HIGHLIGHTS
- who: from the (UNIVERSITY) have published the research: Filtering Specialized Change in a Few-Shot Setting, in the Journal: (JOURNAL)
- what: The authors propose an approach to detect specialized changes that works top-down: First, the authors learn to classify a broader type of change in a binary classification task, for which ample training data are available, and then, try to filter out one particular subcategory via only a few examples, thereby entering the realm of few-shot learning , . As few-shot learning deals with novel classes that the machine_learning model has never seen before . . .
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