2 papers
cs.AI2025
Does Prior Data Matter? Exploring Joint Training in the Context of Few-Shot Class-Incremental Learning
Shiwon Kim, Dongjun Hwang, Sungwon Woo +1
Class-incremental learning (CIL) aims to adapt to continuously emerging new classes while preserving knowledge of previously learned ones. Few-shot class-incremental learning (FSCI…
cs.LG2025
Krait: A Backdoor Attack Against Graph Prompt Tuning
Ying Song, Rita Singh, Balaji Palanisamy
Graph prompt tuning has emerged as a promising paradigm to effectively transfer general graph knowledge from pre-trained models to various downstream tasks, particularly in few-sho…