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...
我们把这种对匹配网络的改进称为原型匹配网络。 4、Meta-Learning with Learned Hallucination 我们现在通过学习产生幻觉的其他例子来介绍我们的低目标学习方法。 给定一个初始训练集Strain,我们想要一种方法来取样额外的幻觉例子。 继最近关于生成建模的工作[11,15]之后,我们将通过对噪声向量作为输入的确定性函数来建模这...
with explanations, definitions of terms, and overviews of approaches to each problem. We define what is meant by “low-shot learning” in this paper (as definitions of few-shot learning are often inconsistent or vague
少样本学习:研究的就是如何从少量样本中去学习。一般用于基础的分类识别任务,常假定有另外样本类别相关且数据量较大的辅助数据集。 Low-shot Learning via Covariance-Preserving Adversarial Augmentation Networks 数据增广思路,数据增广的目标为增广出来的图片能保真,多样。实现思路是对数据生成的过程加以限制。本文的基本...
VIDHOP, viral host prediction with Deep Learning 论文阅读笔记 github : https://github.com/flomock/vidhop 摘要 Zoonosis即人类和动物都可相互传染致病,如寨卡病毒、埃博拉病毒和新冠病毒等,为了预防全球化带来的病毒传染加快的问题,本文提出一种基于病毒的基因组序列来推测病毒宿主的预测方法(input = ...论文...
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...
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 ...
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 ...
These low-shot learning frameworks will reduce the manual image annotation burden and improve semantic segmentation performance for remote sensing imagery. 展开 关键词: Deep learning feature learning hyperspectral imaging self-taught learning semantic segmentation semisupervised ...