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...
四、实验展示 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;...
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
When I look at the workspace at it's class it shows as character however the value is 1x48 char. When I copy the output of that same item and set test= 'text' I see a class of character however the value is 'text' or 'text... (for longer strings). Why are these not the ...
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...
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...
Such an idea can be combined with Mixup, which exploits the interpolation information of data to further compensate for the information of sample space that the typical samples do not provide and expand the role of the proposed re-weighting scheme. Experiments on artificially induced long-tailed ...
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 ...
mixup数据增强在图像领域获得了不错的效果,但是在图数据中仍然有很多挑战。他有这么几个难点:1.不同的图有不同的结点个数;2.图数据并不是对齐的;3.图在非欧空间中有唯一的拓扑结果。所以直接使用mixup是不行的,作者提出了G-mixup,用于图的数据增强。他的主要思想是,同属于一类的graph生成同一个graphon。不同...