Knowledge Commons of Institute of Automation,CAS
Few-Shot Learning via Feature Hallucination with Variational Inference | |
Luo QX(罗沁轩)1,2![]() ![]() ![]() ![]() | |
2021-01 | |
会议名称 | IEEE Winter Conference on Applications of Computer Vision |
会议日期 | 2021-1 |
会议地点 | 线上会议 |
摘要 | Deep learning has achieved huge success in the field of artificial intelligence, but the performance heavily depends on labeled data. Few-shot learning aims to make a model rapidly adapt to unseen classes with few labeled samples after training on a base dataset, and this is useful for tasks lacking labeled data such as medical image processing. Considering that the core problem of few-shot learning is the lack of samples, a straightforward solution to this issue is data augmentation. This paper proposes a generative model (VI-Net) based on a cosine-classifier baseline. Specifically, we construct a framework to learn to define a generating space for each category in the latent space based on few support samples. In this way, new feature vectors can be generated to help make the decision boundary of classifier sharper during the fine-tuning process. To evaluate the effectiveness of our proposed approach, we perform comparative experiments and ablation studies on mini-ImageNet and CUB. Experimental results show that VI-Net does improve performance compared with the baseline and obtains the state-of-the-art result among other augmentation-based methods. |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[61773377] |
语种 | 英语 |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/44310 |
专题 | 多模态人工智能系统全国重点实验室_先进时空数据分析与学习 |
作者单位 | 1.NLPR, Institute of Automation, Chinese Academy of Sciences 2.School of Artificial Intelligence, University of Chinese Academy of Sciences 3.Key Laboratory of Knowledge Automation for Industrial Processes, Ministry of Education 4.School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture |
第一作者单位 | 模式识别国家重点实验室 |
推荐引用方式 GB/T 7714 | Luo QX,Wang LF,Lv JG,et al. Few-Shot Learning via Feature Hallucination with Variational Inference[C],2021. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
Few-Shot Learning vi(679KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
除非特别说明,本系统中所有内容都受版权保护,并保留所有权利。
修改评论