论文题目：A Closer Look at Few-shot Classification Again
报告嘉宾：罗旭（电子科技大学）
讲者简介：
罗旭，电子科技大学计算机学院博士生。研究兴趣目前集中于对大模型的小样本迁移能力（比如finetune和in-context learning）的分析与理解。以一作身份于NeurIPS/ICML发表论文3篇，担任NeurIPS/ICML/CVPR/ICCV/ECCV/AAAI/TIP等会议期刊审稿人。
个人主页：
https://frankluox.github.io/
报告摘要：
Few-shot classification consists of a training phase where a model is learned on a relatively large dataset and an adaptation phase where the learned model is adapted to previously-unseen tasks with limited labeled samples. In this paper, we empirically prove that the training algorithm and the adaptation algorithm can be completely disentangled, which allows algorithm analysis and design to be done individually for each phase. Our meta-analysis for each phase reveals several interesting insights that may help better understand key aspects of few-shot classification and connections with other fields such as visual representation learning and transfer learning. We hope the insights and research challenges revealed in this paper can inspire future work in related directions.
参考文献：
[1] Xu Luo, Hao Wu, Ji Zhang, Lianli Gao, Jing Xu, Jingkuan Song, “A Closer Look at Few-shot Classification Again,” ICML 2023.
