Summer Tree is Cyan

Thinking will not overcome fear but action will.

A Concise Review of Recent Few-shot Meta-learning Methods

小样本元学习

@toc 1 Introduction In this short communication, we present a concise review of recent representative meta- learning methods for few-shot image classification. We re- fer to such methods as few-sh...

少标签数据学习 Few labeled data learning

宾夕法尼亚大学课程

Few-shot image classification Three regimes of image classification Problem formulation Training set consists of labeled samples from lots of “tasks”, e.g., classifying cars, cats, dogs, planes...

Elements of Meta-Learning 关于元学习和强化学习

卡耐基梅隆大学 Probabilistic Graphical Models 课程

Goals for the lecture: Introduction & overview of the key methods and developments. [Good starting point for you to start reading and understanding papers!] Probabilistic Graphical Models...

Probabilistic Graphical Models

Statistical and Algorithmic Foundations of Deep Learning

Probabilistic Graphical Models Statistical and Algorithmic Foundations of Deep Learning Author: Eric Xing 01 An overview of DL components Historical remarks: early days of neural networks 我们...

最新小样本学习综述: A Survey on Few-shot Learning

Multitask Learning、Embedding Learning、Learning with External Memory、Generative Modeling

相关阅读: A Survey on Few-Shot Learning | Introduction and Overview A Survey of Few-Shot Learing | Data 给定少数样本的,仅使用简单模型(例如线性分类器)就可以选择较小的H (假设空间)[92,94]。 但是,现实世界中的问题通常很复杂,并且不能由小H的假设h很好地表示[45]。 因此,在FSL中...

A Survey on Few-shot Learning | Data

当前最新小样本学习综述

本节中的FSL方法使用先验知识来增强数据,从而丰富了E中的监督信息。(图4)。 Data augmentation via hand-crafted rules is usually used as pre-processing in FSL methods. They can introduce different kinds of invariance for the model ...

A Survey on Few-shot Learning | Introduction and Overview

当前最新小样本学习综述

Author list YAQING WANG, Hong Kong University of Science and Technology and Baidu Research QUANMING YAO∗, 4Paradigm Inc. JAMES T. KWOK, Hong Kong University of Science and Technology LIONEL M. ...

Graph Neural Networks图神经网络(一)

Author: Nihai V. Nayak (March 2020) @toc 01 Introduction Graph Neural Networks (GNN) is a type of neural network which learns the structure of a graph. Learning graph structure allows us...

Capsule Networks胶囊网络(一)

author: Sargur Srihari srihari@buffalo.edu This is part of lecture slides on Deep Learning: http://www.cedar.buffalo.edu/~srihari/CSE676 @[toc] Limitations of Convolutional Networks Convolut...

无监督小样本学习渐进式标签优化算法设计

Unsupervised small sample learning

基本思路 保留01标签 尽量避免采用错误标记数据进行训练,融入渐进式采样 用绝对距离取代相对距离 算法流程 基础设定 为了方便解释算法和画图演示,我们假设训练集共k类,k=3,每类有n个原始样本,n=2,每个原始样本增强c个样本,c=3. 也就是说,共3x2=6个样本簇,每个簇共3+1=4个样本,总共是6*4=24个样本。 目标:学习6个样本簇之间的同类关系。 Inpu...