Improving Adversarial Robustness via Channel-Wise Activation Suppressing

目录概主要内容代码 Bai Y., Zeng Y., Jiang Y., Xia S., Ma X., Wang Y. Improving adversarial robustness via channel-wise activation suppressing. In International Conference on Learning Representations (ICLR), 2021. Yan H., Zhang J., Niu G., Feng J., Tan V.,…

Improving Adversarial Robustness Using Proxy Distributions

目录概主要内容 proxy distribution 如何利用构造的数据 Sehwag V., Mahloujifar S., Handina T., Dai S., Xiang C., Chiang M. and Mittal P. Improving adversarial robustness using proxy Distributions. arXiv preprint arXiv: 2104.09425, 2021. 概本文利用GAN生成数据, 并利用这些数据进行对抗训练,…

IMPROVING ADVERSARIAL ROBUSTNESS REQUIRES REVISITING MISCLASSIFIED EXAMPLES

目录概主要内容符号 MART Wang Y, Zou D, Yi J, et al. Improving Adversarial Robustness Requires Revisiting Misclassified Examples[C]. international conference on learning representations, 2020. @article{wang2020improving, title={Improving Adversarial Robustn…

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks

目录概主要内容 Auto-PGD Momentum Step Size 损失函数 AutoAttack Croce F. & Hein M. Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. In International Conference on Machine Learning (ICML), 2020. 概作者改进了PGD攻击方法, 并…

Second Order Optimization for Adversarial Robustness and Interpretability

目录概主要内容 (4)式的求解超参数 Tsiligkaridis T., Roberts J. Second Order Optimization for Adversarial Robustness and Interpretability. arXiv preprint axXiv 2009.04923, 2020. 概也算是一种对抗训练吧, 有区别的是构造对抗样本的方式, 以及用的是惩罚项而非仅用对抗样本训练. 主要内容考虑干净样本\(x\)和扰动\(v\), 则我们自然希望 \…

Certified Adversarial Robustness via Randomized Smoothing

目录概主要内容定理1 代码 Cohen J., Rosenfeld E., Kolter J. Certified Adversarial Robustness via Randomized Smoothing. International Conference on Machine Learning (ICML), 2019. @article{cohen2019certified, title={Certified Adversarial Robustness via Randomiz…

Inherent Adversarial Robustness of Deep Spiking Neural Networks: Effects of Discrete Input Encoding and Non-Linear Activations

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! arXiv:2003.10399v2 [cs.CV] 23 Jul 2020 ECCV 2020 1 https://github.com/ssharmin/spikingNN-adversarial-attack Abstract 在最近对可信任的神经网络的探索中,我们提出了一个潜在的候选,即脉冲神经网络(SNN)之于对抗攻击的内在鲁棒性.在这项工作中,我们证明对CIFAR数据集上的深度VGG和ResNet结构,在基于梯度的攻击下,…

Generative Adversarial Networks overview（1）

Libo1575899134@outlook.com Libo (原创文章,转发请注明作者) 本文章会先从Gan的简单应用示例讲起,从三个方面问题以及解决思路覆盖25篇GAN论文,第二个大部分会进一步讲Gan的所有领域应用 ----------------------------------------------------------------------------------- 1: 下图GAN可以学到不同的字体,并且在字体之间进行不同的变换 2 下图可以用简笔画可以用GAN帮助生成想…

RCAN——Image Super-Resolution Using Very Deep Residual Channel Attention Networks

1. 摘要在图像超分辨领域,卷积神经网络的深度非常重要,但过深的网络却难以训练.低分辨率的输入以及特征包含丰富的低频信息,但却在通道间被平等对待,因此阻碍了网络的表示能力. 为了解决上述问题,作者提出了一个深度残差通道注意力网络(RCAN).特别地,作者设计了一个残差中的残差(RIR)结构来构造深层网络,每个 RIR 结构由数个残差组(RG)以及长跳跃连接(LSC)组成,每个 RG 则包含一些残差块和短跳跃连接(SSC). RIR 结构允许丰富的低频信息通过多个跳跃连接直接进行传播,使主网络专…

Adversarial Detection methods

目录 Kernel Density (KD) Local Intrinsic Dimensionality (LID) Gaussian Discriminant Analysis (GDA) Gaussian Mixture Model (GMM) SelectiveNet Combined Abstention Robustness Learning (CARL) Adversarial Training with a Rejection Option Energy-based Out-of…

2016CVPR论文集

http://www.cv-foundation.org/openaccess/CVPR2016.py ORAL SESSION Image Captioning and Question Answering Monday, June 27th, 9:00AM - 10:05AM. These papers will also be presented at the following poster session 1 Deep Compositional Captioning: Descr…

CVPR2016 Paper list

CVPR2016 Paper list ORAL SESSIONImage Captioning and Question Answering Monday, June 27th, 9:00AM - 10:05AM. These papers will also be presented at the following poster session 1 Deep Compositional Captioning: Describing Novel Object Categories Witho…

(转) Summary of NIPS 2016

转自:http://blog.evjang.com/2017/01/nips2016.html Eric Jang Technology, A.I., Careers Monday, January 2, 2017 Summary of NIPS 2016 The 30th annual Neural Information Processing Systems (NIPS) conference took place in Barcelona…

yaffs文件系统

1. 概述yaffs文件系统专为Nandflash设计的日志文件系统,占用page中oob区域.目前有两个版本的yaffs文件系统.nandflash不可靠,存在坏块,存在数据错误,需要软件弥补纠正此错误.在yaffs中分配单元为CHUNK(chunk),一般情况,典型情况下其值与一页(page)大小相同(同时可灵活配置).yaffs中文件数据的存储以chunk为单位.YAFFS是专为为NandFlash设计的文件系统,基于MTD,为单线程程序.官网:http://www.yaffs.net/y…

Competing in a data science contest without reading the data

Competing in a data science contest without reading the data Machine learning competitions have become an extremely popular format for solving prediction and classification problems of all sorts. The most famous example is perhaps the Netflix prize.…

Res-Family: From ResNet to SE-ResNeXt

Res-Family: From ResNet to SE-ResNeXt 姚伟峰 http://www.cnblogs.com/Matrix_Yao/ Res-Family: From ResNet to SE-ResNeXt ResNet(2015 Dec) Paper Network Visualization Problem Statement Why Conclusion How to Solve it Breakdown Residule Module Identity Shortc…

Glossary Collection

目录直接修饰用间接强调用 (多为副词) 过渡用特别的名词动词词组各种介词句子摘要引言总结正文实验直接修饰用 Word 含义例句近义词 nuanced adj. 微妙的:具有细微差别的; v. 精确细腻地表演:细致入微地描绘 However, a more nuanced understanding of images arguably requires the ability to reason about how the scene depicted in the…

iPhone激活策略说明

本帖最后由苏州汇东于 2014-7-2 19:13 编辑奉告各位封釉千万不要泄露机器序列号IMEI号远程ID真的可以上只要机器上没有ID 就可以远程上任何ID 我这可以远程上ID 也帮忙解ID 查询完整ID账号信息以前威锋ID是放荡人人由于发回收手机帖子被封了 ... 激活策略说明 ID:79 激活策略说明:Hong Kong and Macau Apple Channel Default activation policyID:306 激活策略说明:Hong Kong CS…

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization 2019-10-10 10:50:19 Paper: http://openaccess.thecvf.com/content_ICCV_2017/papers/Huang_Arbitrary_Style_Transfer_ICCV_2017_paper.pdf Officical Torch Code: https://github.com/xu…

注意机制CBAM

这是一种用于前馈卷积神经网络的简单而有效的注意模块. 给定一个中间特征图,我们的模块会沿着两个独立的维度(通道和空间)依次推断注意力图,然后将注意力图乘以输入特征图以进行自适应特征修饰. 由于CBAM是轻量级的通用模块,因此可以以可忽略的开销将其无缝集成到任何CNN架构中,并且可以与基础CNN一起进行端到端训练. 为了实现这一目标,我们依次应用频道和空间关注模块(如图1所示),以便每个分支机构都可以分别学习在频道和空间轴上参与的“内容”和“位置”.结果,我们的模块通过学习要强调或抑制的信息来有效…

CSAR——Channel-wise and Spatial Feature Modulation Network for Single Image Super-Resolution

1. 摘要 CNN 中的特征包含着不同类型的信息,它们对图像重建的贡献也不一样.然而,现在的大多数 CNN 模型却缺少对不同信息的辨别能力,因此也就限制了模型的表示容量. 另一方面,随着网络的加深,来自前面层的长期信息很容易在后面的层被削弱甚至消失,这显然不利于图像的超分辨. 作者提出了一个通道和空间特征调制(CSFM)网络,其中一系列特征调制记忆(FMM)模块级联在一起来将低分辨率特征转化为高信息量的特征.而在每个 FMM 内部,则集成了许多通道和空间注意力残差块(CSAR)以及一个用来保留长…

论文翻译——Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection

Dynamic Pooling and Unfolding Recursive Autoencoders for Paraphrase Detection 动态池和展开递归自动编码器的意译检测论文地址 Richard Socher,Eric H. Huang, Jeffrey Pennington∗ , Andrew Y. Ng, Christopher D. Manning Computer Science Department, Stanford University, Stanford,…

SNN对抗攻击笔记

SNN对抗攻击笔记: 1. 解决SNN对抗攻击中脉冲与梯度数据格式不兼容性以及梯度消失问题: G2S Converter.Gradient Trigger[1] 2. 基于梯度的对抗攻击方式: FGSM[1-3] R-FGSM[3] I-FGSM[3] BIM[1] PGD[2] 3. 图像转化到脉冲序列的采样方式: Poisson事件生成过程[1-9] 4. SNN类型: SNN-conv(ANN-SNN Conversion)[2-7, 9] 采用的神经元模型 IF神经元模型[2-7, 9]…

对抗性鲁棒性与模型压缩：ICCV2019论文解析

对抗性鲁棒性与模型压缩:ICCV2019论文解析 Adversarial Robustness vs. Model Compression, or Both? 论文链接: http://openaccess.thecvf.com/content_ICCV_2019/papers/Ye_Adversarial_Robustness_vs._Model_Compression_or_Both_ICCV_2019_paper.pdf Code is available at https://githu…