Adaptive Confidence Smoothing for Generalized Zero-Shot Learning
Adaptive Confidence Smoothing for Generalized Zero-Shot Learning
Generalized zero-shot learning (GZSL) is the problem of learning a classifier where some classes have samples and others are learned from side information, like semantic attributes or text description, in a zero-shot learning fashion (ZSL). Training a single model that operates in these two regimes simultaneously is challenging. Here we …