Several works focus on techniques to boost the efficiency of existing architectures by data augmentation, regularization, etc. In this work, we investigate using mixup data augmentation [21] in order to improve the efficiency of DL-based SideChannel Attacks (SCAs). We validated the soundness of ...
MIXUP AMDA 总结 对抗学习和数据增强,两个世界的交汇 摘要 预训练语言模型 (PLM) 在对抗性攻击下表现不佳。 为了提高对抗性鲁棒性,对抗性数据增强(ADA)被广泛采用,通过在训练过程中添加文本对抗性示例来覆盖对抗性攻击的更多搜索空间。 然而,由于攻击搜索空间呈指数级增长,文本增强的对抗样本数量仍然非常不足。 在...
大量的实验表面G-mixup可以提高GNN的泛化能力和鲁棒性 1.Mixup数据增强 在这里插入图片描述 mixup的思想很简单,这里的$\lambda$是服从beta分布,范围为(0,1)的连续概率密度函数。因为图像数据都是方阵,所以很好进行变换。思想简单但是取得了很不错的效果。作者就想着能不能把mixup迁移到图的数据上来。 但是有几个难...
最近工作的视频讲解来啦～大佬们多多关注鸭～～～Title: Meta-FDMixup: Cross-Domain Few-Shot Learning Guided byLabeled Target Data. (ACM MM 2021)Paper Link: https://arxiv.org/abs/2107.11978Code Link: https://github.com/lovelyqi
Official PyTorch implementation of "GuidedMixup An Efficient Mixup Strategy Guided by Saliency Maps" (AAAI'23 Oral) - GuidedMixup/load_data.py at main · kdst-team/GuidedMixup
Mixup, a recent proposed data augmentation method through linearly interpolating inputs and modeling targets of random samples, has demonstrated its capability of significantly improving the predictive accuracy of the state-of-the-art networks for image classification. However, how this technique can be...
Sorry, not sure how this slipped by. It would always be trying to combine old and new data, even if new data has indeed been updated to include a larger time range. Should be low-impact or no-impa...
1. 首先,本文提出了 G-Mixup 来扩充用于图分类的训练图。由于直接混合图是难以处理的,因此 G-Mixup 将不同类别的图的图元混合以生成合成图。 2. 其次,本文从理论上证明合成图将是原始图的混合,其中源图的关键拓扑(即判别主题)将被混合。 3. 最后,本文证明了所提出的 G-Mixup 在各种图神经网络和数据集上的...
Data Augmentation Techniques: Mixup, Cutout, Cutmix This article will briefly describe the above image augmentations and their implementations in Python for the PyTorch Deep Learning framework. Setup This tutorial will use a toy example of a "vanilla" image classification problem. The task is ...
阅读论文第2篇:《InsMix: Towards Realistic Generative Data Augmentation for Nuclei Instance Segmentation》 一、Mixup数据增强 基于mixip这种数据增强方法,大概可以分为如下几种: Mixup:就是将两张patch的像素加权平均; Cutout:将原patch一部分抹掉; Cutmix:从一张patch裁剪一小部分贴到新的patch上; ...