目录 概 主要内容 代码 Wang Y., Huang G., Song S., Pan X., Xia Y. and Wu C. Regularizing Deep Networks with Semantic Data Augmentation. TPAMI. 概 通过data augments来对数据进行扩充, 可以有效提高网络的泛化性. 但是这些transformers通常只有一些旋转, 剪切等较为简单的变换, 想要施加更为复杂的语义不变变换(如切换背景), 可能就需要GAN等引入额外的…

B. McMahan, E. Moore, D. Ramage, S. Hampson, and B. A. y Arcas, "Communication-Efficient Learning of Deep Networks from Decentralized Data," in Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Apr. 2017…

郑重声明:原文参见标题,如有侵权,请联系作者,将会撤销发布! Proceedings of the 20th International Conference on Artificial Intelligence and Statistics (AISTATS) 2017, Fort Lauderdale, Florida, USA. JMLR: W&CP volume 54. Copyright 2017 by the author(s). Abstract 现代移动设备可以访问大量适合模型学…

论文信息 论文标题:Data Augmentation for Deep Graph Learning: A Survey论文作者:Kaize Ding, Zhe Xu, Hanghang Tong, Huan Liu论文来源:2022, arXiv论文地址:download 1 介绍 本文主要总结图数据增强,并对该领域的代表性方法做出归类分析. DGL 存在的两个问题: 次优图问题:图中包含不确定.冗余.错误和缺失的节点特征或图结构边. 有限标签问题:标签数据成本高,目前大部分 DGL 方法是…

1. 在深度学习中,当数据量不够大时候,常常采用下面4中方法:  (1)人工增加训练集的大小. 通过平移, 翻转, 加噪声等方法从已有数据中创造出一批"新"的数据.也就是Data Augmentation (2)Regularization. 数据量比较小会导致模型过拟合, 使得训练误差很小而测试误差特别大. 通过在Loss Function 后面加上正则项可以抑制过拟合的产生. 缺点是引入了一个需要手动调整的hyper-parameter. 详见https://www.wikiwan…

Fully Convolutional Networks for Semantic Segmentation 译文 Abstract   Convolutional networks are powerful visual models that yield hierarchies of features. We show that convolutional networks by themselves, trained end-to-end, pixels-to-pixels, exceed…

前言 1.理论知识:UFLDL教程.Deep learning:十六(deep networks) 2.实验环境:win7, matlab2015b,16G内存,2T硬盘 3.实验内容:Exercise: Implement deep networks for digit classification.利用深度网络完成MNIST手写数字数据库中手写数字的识别.即:用6万个已标注数据(即:6万张28*28的图像块(patches)),作为训练数据集,然后把它输入到栈式自编码器中,它的第一层自编码器…

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks ICML 2017 Paper:https://arxiv.org/pdf/1703.03400.pdf Code for the regression and supervised experiments:https://github.com/cbfinn/maml Code for the RL experiments:https://github.com/cb…

Exercise: Implement deep networks for digit classification 习题链接:Exercise: Implement deep networks for digit classification stackedAEPredict.m function [pred] = stackedAEPredict(theta, inputSize, hiddenSize, numClasses, netconfig, data) % stackedAEPre…

In recent years, there’s been a resurgence in the field of Artificial Intelligence. It’s spread beyond the academic world with major players like Google, Microsoft, and Facebook creating their own research teams and making some impressive acquisition…