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Zero and Few Shot Learning With Semantic Feature Synthesis and Competitive Learning.
IEEE Transactions on Pattern Analysis and Machine Intelligence
|January 16, 2020
Summary
This study introduces a new method for zero-shot learning (ZSL) and few-shot learning (FSL) by synthesizing unseen class data and using competitive bidirectional projection learning (BPL). The approach enhances model robustness and achieves state-of-the-art results on both ZSL and FSL tasks.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Zero-shot learning (ZSL) relies on projection functions between feature and semantic spaces.
- A significant challenge in ZSL is the domain gap between seen and unseen classes.
- Robust projection learning is crucial for effective ZSL.
Purpose of the Study:
- To develop a robust projection function for zero-shot learning (ZSL) that addresses the domain gap.
- To propose a novel semantic data synthesis strategy for generating unseen class data.
- To extend the proposed ZSL model to few-shot learning (FSL).
Main Methods:
- A novel semantic data synthesis strategy using class prototypes to perturb seen data for unseen class generation.
- Competitive bidirectional projection learning (BPL) model to handle ambiguities in synthesized data.
- Extension to few-shot learning (FSL) via semantic feature synthesis and competitive BPL.
Main Results:
- The proposed semantic data synthesis effectively generates unseen class data.
- The competitive BPL model robustly utilizes ambiguous synthesized data for projection learning.
- The ZSL model successfully extends to FSL, achieving state-of-the-art performance.
Conclusions:
- The developed approach effectively bridges the domain gap in ZSL through data synthesis and robust projection learning.
- The competitive BPL framework demonstrates superior performance in handling synthesized data.
- The model's adaptability to few-shot learning further validates its effectiveness and versatility.
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