[论文笔记] Intriguing properties of neural networks 说在前面 个人心得: 该paper主要是发现了以下两个有趣的性质 神经网络中携带语义信息的不是某单个神经元,而是整个网络(或者说那一层)所表示的空间 给样本添加一些轻微的扰动,会导致神经网络模型错误分类,这些样本就称为对抗样本(一般认为这篇paper是对抗样本...
论文题目:Intriguing properties of neural networks 论文地址:https://arxiv.org/pdf/1312.6199.pdf 代码地址:待更 该论文发表在ICLR 2014上,是对抗样本(adversarial examples)领域的开山之作,主要探究了神经网络两个违反直觉的性质: 神经网络中携带语义信息的不是某单个神经元,而是整个网络所表示的空间。 给样本添加...
1. 中心思想 我们小组阅读了Intriguing properties of neural networks,本文主要介绍了两个方面一个是神经网络的语义信息不存在于独立的神经元中,而存在于整个神经元激活的空间内。另外一个是神经网络的盲点---对抗样本的存在性 2. 数据集和网络结构 本文用到的数据集如下: 1. MNIST dataset ...
Intriguing properties of neural networks 笔记 Intriguing properties of neural networks https://arxiv.org/abs/1312.6199 Summary 本文首次提出了对抗样本这一概念并且给出一个基于Box-constrained L-BFGS的白盒攻击的梯度方法来生成对抗样本。 同时,本文论证了深层神经网络中语义信息是基于整个网络的,并非某一个层的...
Intriguing Properties of Neural Networks Introduction: Neural networks are a type of machine learning model inspired by the human brain's functioning. They are composed of interconnected nodes known as neurons that work together to process and analyze complex data. Neural networks have gained immense ...
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutio
Deep neural networks are highly expressive models that have recently achieved state of the art performance on speech and visual recognition tasks. While their expressiveness is the reason they succeed, it also causes them to learn uninterpretable solutions that could have counter-intuitive properties. ...
Intriguing properties of neural networks. arXiv preprint arXiv:13126199. 2013;.Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., and Fergus, R. Intriguing properties of neural networks. December 2013....
intriguing properties of neural networks 精读intriguing properties of neural networks精读 神经网络具有许多令人着迷的特性。以下是其中一些: 1.非线性映射能力:神经网络可以通过组合多个非线性函数来实现复杂的非线性映射。这使得神经网络在解决非线性问题方面具有很大的优势。 2.自适应性:神经网络可以根据输入数据的...
对抗样本开山之作!-Intriguing properties of neural networks 刘嘉威 机器学习 人工智能11 人赞同了该文章 总结 首先Szegedy发现在high layers是神经元构成的space而非单个神经元包含着语义信息。 其次我们发现NN学到的输入输出映射是相当不连续的(不对抗鲁棒)。并且发现对抗扰动可以在训练在不同数据集上的不同结构NN间...