论文下载:Low-Shot Learning from Imaginary Data 本文由Facebook、卡内基梅隆大学和康奈尔大学合作完成。其中包括了大名鼎鼎的Ross Girshick(R-CNN等网络的发明人)。 主要从以下几个方面来解读论文: 1 论文的出发点(其他论文的不足之处) 2 本文的贡献 3 论文的创新点(提出的结构或方法) 4 改进的思路 (模型实现...
Low-shot也叫做lifelong learning,一般分为base category和novel category,其中novel category每类只有少量样本(K-shot),希望模型在测试集上的base category和novel category都表示很好。 一种常见的思路是两阶段训练网络:stage-1对base category进行训练,得到一个特征提取器。stage-2根据novel category的训练样本(K-shot...
首先,在表示学习阶段,基于卷积神经网络(ConvNet)的特征提取器在一组类上进行训练,每个类有数千个样本; 这个集合称为“基”类Cbase。 然后,在low-shot学习阶段,识别系统遇到一组额外的“新”类Cnovel,每个类有少量的例子n。 它还可以访问基类训练集。 系统现在必须学会识别基础类和新类。 在一个包含这两组类...
The tasks of few-shot, one-shot, and zero-shot learning—or collectively “low-shot learning” (LSL)—at first glance are quite similar to the long-standing task of class imbalanced learning; specifically, they aim to learn classes for which there is little labeled data available. Motivated ...
Low-Shot Learning from Imaginary Data 摘要 人类可以快速学习新的视觉概念,也许是因为他们可以很容易地从不同的角度想象出新的物体的样子。结合这种对新概念产生幻觉的能力,可能有助于机器视觉系统进行更好的低视角学习,也就是说,从少数例子中学习概念。我们提出了一种新的低镜头学习方法,使用这个想法。我们的方法...
大多数基于 Meta-Learning 的 Few-shot Learning 方法都包含一个特征提取器和一个分类器。特征提取器将原始样本嵌入到一个合理的特征空间,特征分类器在这个空间中进行时分类。 当前 Meta-Learning 中主流的分类器一般是基于近邻的分类器,本文作者认为小样本情况下,线性分类器要优于近邻分类器。因此本文使用线性分类器...
Low-shot Learning attempts to address these drawbacks. Low-shot learning allows the model to obtain good predictive power with very little or no training data, where structured knowledge plays a key role as a high-level semantic representation of human. This article will review the fundamental ...
论文阅读笔记《Meta R-CNN : Towards General Solver for Instance-level Low-shot Learning》,程序员大本营,技术文章内容聚合第一站。
In this work, we propose Covariance-Preserving Adversarial Augmentation Networks to overcome existing limits of low-shot learning. Specifically, a novel Generative Adversarial Network is designed to model the latent distribution of each novel class given its related base counterparts. Since direct ...
In this paper, we study low-shot learning using self-taught feature learning for semantic segmentation. We introduce 1) an improved self-taught feature learning framework for HSI and MSI data and 2) a semi-supervised classification algorithm. When these are combined, they achieve state-of-the-...