Incremental Learning: Comparing Methods for Catastrophic Forgetting and Model Promotion
Instance-Incremental Learning (IIL) Overview
This section reviews research on Instance-Incremental Learning (IIL), highlighting its distinction from the better-studied Class-Incremental Learning. IIL focuses on managing new instances within existing classes, offering novel approaches for efficient resource use.
Innovations in Instance Management
Pioneering methods in instance management foster innovative solutions that improve resource efficiency and support sustainable model updates.
Data Scarcity Solution
One notable approach is S-CycleGAN, designed for CT-to-Ultrasound image translation, addressing challenges related to limited data availability.
Data scarcity remains a critical challenge, and S-CycleGAN offers a promising solution for cross-modality medical imaging translation.
Author's summary: This article explores methods in Instance-Incremental Learning, emphasizing innovative instance management and data scarcity solutions like S-CycleGAN to enhance model adaptation.