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A review of generalized zero-shot learning methods
Generalized zero-shot learning (GZSL) aims to train a model for classifying data samples
under the condition that some output classes are unknown during supervised learning. To …
under the condition that some output classes are unknown during supervised learning. To …
Generalized zero-shot learning via over-complete distribution
A well trained and generalized deep neural network (DNN) should be robust to both seen
and unseen classes. However, the performance of most of the existing supervised DNN …
and unseen classes. However, the performance of most of the existing supervised DNN …
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 …
classes have samples and others are learned from side information, like semantic attributes …
Dual progressive prototype network for generalized zero-shot learning
Abstract Generalized Zero-Shot Learning (GZSL) aims to recognize new categories with
auxiliary semantic information, eg, category attributes. In this paper, we handle the critical …
auxiliary semantic information, eg, category attributes. In this paper, we handle the critical …
Part-object progressive refinement network for zero-shot learning
Zero-shot learning (ZSL) recognizes unseen images by sharing semantic knowledge
transferred from seen images, encouraging the investigation of associations between …
transferred from seen images, encouraging the investigation of associations between …
Evolving semantic prototype improves generative zero-shot learning
In zero-shot learning (ZSL), generative methods synthesize class-related sample features
based on predefined semantic prototypes. They advance the ZSL performance by …
based on predefined semantic prototypes. They advance the ZSL performance by …
Dual adversarial semantics-consistent network for generalized zero-shot learning
Generalized zero-shot learning (GZSL) is a challenging class of vision and knowledge
transfer problems in which both seen and unseen classes appear during testing. Existing …
transfer problems in which both seen and unseen classes appear during testing. Existing …