小样本学习(Few-shot learning)的目标是通过从基础知识中学习,最终识别具有有限支持样本的新查询。小样本学习假设基础知识和新查询样本在相同的领域中分布,虽然最近在这个设定下取得了一些进展,但对于实际应用来说这通常是不可行的。针对这个问题,本文提出解决跨领域小样本学习问题的方法,其中在目标领域中只有极少量的样...
Few-shotImage classificationEmbedded adaptiveCross-modulationAlthough deep neural networks have made great success in several scenarios of machine learning, they face persistent challenges in small training datasets learning scenarios. Few-shot learning aims to learn from a few labeled examples. However, ...
Task-aware Adaptive Learning for Cross-domain Few-shot Learning Yurong Guo1, Ruoyi Du1, Yuan Dong1, Timothy Hospedales2, Yi-Zhe Song3, Zhanyu Ma1* 1Beijing University of Posts and Telecommunications, China 2University of Edinburgh, UK 3University of Surrey, UK...
Most few-shot learning works rely on the same domain assumption between the base and the target tasks, hindering their practical applications. This paper proposes an adaptive transformer network (ADAPTER), a simple but effective solution for cross-domain few-shot learning where there exist large ...
Although deep neural networks have made great success in several scenarios of machine learning, they face persistent challenges in small training datasets learning scenarios. Few-shot learning aims to learn from a few labeled examples. However, the limited training samples and weakly distinguishable embe...
现有解决方法:Transfer learning、few-shot learning、meta-learning等介绍了如何解决机器学习中的数据稀缺问题。由于每个对话域之间的差异很大,因此将信息从富资源域泛化到另一个低资源域非常困难。 本文的方法:所以本文提出了一种基于元学习(meta-learning )的领域自适应对话生成方法DAML。DAML是一个端到端可训练的对话...
作者将Optimization as a model for few-shot learning中的convnet改造为routed版本,并在3个图像分类数据集上实验。一个标签相当于一个任务。 对比算法就是cross-stitch networks和Caruana介绍的joint training strategy with layer sharing。 数值结果我们就不说了。我们看一看qualitative experiments中的一个。通过对MNIS...
只输入base learner权重/ 只输入gradient/ 全部都输入 最后当然是全部都输入最好 最后还有两组额外型补充实验 Few-shot regression 上ALFA表现不错 还把α和β具体取值做统计, 按步数排列 文章不错 但是代码基于MAML++, 也可以看出来尽可能用别人效果已经比较好的代码会更容易实现一些....
论文阅读笔记:Few-shot Classification via Adaptive Attention 总的来说,现阶段大多数小样本学习都是基于metric learning或者meta learning。本文提出一种meta learning的方法,将注意力应用到小样本的场景。另外,这篇文章虽然标题挂着个attention,但整篇文章和attention (attention is all you need里的attention) 没啥关系...
PAPER: Task-Adaptive Negative Envision for Few-Shot Open-Set RecognitionCODE: https://github.com/shiyuanh/TANE目的解决小样本开集识别问题 背景传统开集识别需要大量的已知样本,避免过拟合,正确估计分布…