本文先介绍 Few-shot Learning 定义;由于最近几年 Few-shot Learning 在图像领域的进展领先于在自然语言处理领域,所以第二部分结合其在图像处理领域的研究进展,详细介绍 Few-shot Learning 的三类典型方法及每种方法的代表性模型;接下来介绍在自然语言处理领域的研究进展以及我们对 metric-based 的方法进行系统总结后提...
根据机器学习模型在小样本上难以学习的原因,Few-Shot Learning从三个角度解决问题,(1)通过增多训练数据提升h_I(Data)、(2)缩小模型需要搜索的空间(Model)、以及(3)优化搜索最优模型的过程(Algorithm)。 PS: 上面两张图均引自2020年香港科技大学和第四范式的paper“Generalizingfrom a Few Examples: A Survey on...
因此,这篇paper基于这样一个背景,提出了一种新的少样本学习方法:Interventional Few-Shot Learning (IFSL),这个方法的理论是基于预训练知识、少镜头样本和类标签之间的因果关系的假设。具体来讲,Contributions如下: 从结构因果模型(SCM)假设开始,探索了FSL中的一个“复杂性悖论”:该假设表明预先训练的知识本质上是一个...
接上一篇文章 小样本学习(Few-shot Learning)综述(一) 公众号 AI末班车:小样本学习(Few-shot Learning)综述(一) 该文已同步发布在: 小样本学习(Few-shot Learning)综述(二) 论文题目:《Generalizing from…
比较one/fewshot learning的方法一般采用Omniglot和miniImagenet两个数据集,由于前者相对比较简单,准确率已经比较容易达到99%,所以这里只给出miniImagenet上的对比测试结果。miniImagenet的数据集从 https://drive.google.com/file/d/0B3Irx3uQNoBMQ1FlNXJsZUdYWEE/view 这里下载。
Origin paper Few-shot Learning with Retrieval Augmented Language Models Gautier Izacard, Patrick Lewis, M. Lomeli, Lucas Hosseini, F. Petroni, Timo Schick, Jane A. Yu, Armand Joulin, Sebastian Riedel, Edouard Grave 2022 Improving language models by retrieving from ...
One of the earliest works was from the paper “Language Models are Few Shot Learners” by Tom B. Brown et al challenging the need for extensive fine-tuning. A diagram illustrating the difference between supervised learning and few-shot learning approaches. The supervised learning section shows a...
based on auxiliary task co-training to learn a task-dependent metric space. The resulting few-shot learning model based on the task-dependent scaled metric achieves state of the art on mini-Imagenet. We confirm these results on another few-shot dataset that we introduce in this paper based ...
Tasks Edit Few-Shot Learning Meta-Learning Metric Learning Datasets Edit Multi-Domain Sentiment Results from the Paper Edit Submit results from this paper to get state-of-the-art GitHub badges and help the community compare results to other papers. Methods...
26 Tasks Edit AddRemove Datasets Edit Introduced in the Paper: XStoryCloze Used in the Paper: SuperGLUEXNLIBookCorpusCOPAPAWS-XROCStoriesARC (AI2 Reasoning Challenge)CC100XCOPAFLoRes-101StoryCloze Results from the Paper AddRemove Submitresults from this paperto get state-of-the-art GitHub badges ...