Semantic Mask Reconstruction and Category Semantic Learning for few-shot image generation

Area of Science:

• Computer Science
• Artificial Intelligence
• Machine Learning

Background:

• Few-shot image generation aims to create novel images for unseen categories using limited examples.
• Current methods struggle with generating high-quality and diverse images due to challenges in semantic comprehension and representation extraction.

Purpose of the Study:

• To propose a novel method, semantic mask reconstruction (SMR) and category semantic learning (CSL), to enhance few-shot image generation.
• To improve the quality, diversity, and fidelity of generated images in few-shot learning scenarios.

Main Methods:

• Semantic Mask Reconstruction (SMR): Performs mask reconstruction in a semantic space with a dynamically adjusted mask ratio to enhance discriminator learning.
• Category Semantic Learning (CSL): Utilizes triplet loss to optimize inter-category distances, improving fine-grained generation.
• Both SMR and CSL are designed as plug-and-play modules.

Main Results:

• The proposed SMR-CSL method significantly outperforms existing approaches in generating higher quality and more diverse images across three standard datasets.
• Downstream classification experiments confirm the effectiveness of SMR-CSL generated images in assisting classification tasks.

Conclusions:

• SMR and CSL effectively address the limitations of current few-shot image generation techniques.
• The SMR-CSL method offers a robust and versatile solution for generating semantically rich and diverse images, with practical applications in downstream tasks.