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Generalized Zero-Shot Learning

In generalized zero shot learning (GZSL), the set of classes are split into seen and unseen classes, where training relies on the semantic features of the seen and unseen classes and the visual representations of only the seen classes, while testing uses the visual representations of the seen and unseen classes.

Papers

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Showing 81–90 of 161 papers

TitleStatusHype
Fine-grained Event Classification in News-like Text Snippets - Shared Task 2, CASE 2021—0
Learn from Anywhere: Rethinking Generalized Zero-Shot Learning with Limited Supervision—0
Mitigating Generation Shifts for Generalized Zero-Shot LearningCode0
Generalized Zero-Shot Learning using Multimodal Variational Auto-Encoder with Semantic Concepts—0
Multiple-Input Multiple-Output Fusion Network For Generalized Zero-Shot Learning—0
Task-Independent Knowledge Makes for Transferable Representations for Generalized Zero-Shot Learning—0
Rectification-based Knowledge Retention for Continual Learning—0
A Simple Approach for Zero-Shot Learning based on Triplet Distribution Embeddings—0
Learning without Seeing nor Knowing: Towards Open Zero-Shot Learning—0
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning—0
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