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Generative Dataset Distillation: Balancing Global Structure and Local Details
In this paper, we propose a new dataset distillation method that considers balancing global structure and local details when distilling the information from a large dataset into a generative model.
Enhancing Generative Class Incremental Learning Performance with Model Forgetting Approach
This study presents a novel approach to Generative Class Incremental Learning (GCIL) by introducing the forgetting mechanism, aimed at dynamically managing class information for better adaptation to streaming data.
Few-shot Personalized Saliency Prediction Based on Inter-personnel Gaze Patterns
To efficiently treat the PSMs of other persons, this paper focuses on the selection of images to acquire eye-tracking data and the preservation of structural information of PSMs of other persons.
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