Black-boxError diagnosisMachine learningEvaluationMetricsThe application of Deep Neural Networks (DNNs) to a broad variety of tasks demands methods for coping with the complex and opaque nature of these architectures. The analysis of performance can be pursued in two ways. On one side, model ...
用信息论解释深度神经网络!Opening the black box of Deep Neural Networks via Information 引用破千 Hao Bai 人工智能话题下的优秀答主 标题可能夸大或与内容不符 74 人赞同了该文章 目录 收起 文章资源 摘要 引入 研究结果预览 与 文章结构 深度学习的信息论基础 互信息(Mutual Information) 信息...
Opening the black box of Deep Neural Networks via Information 引用破千74 赞同 · 3 评论文章 我觉得值得一看。 这篇文章,以互信息为载体,研究了神经网络的学习目标。通过大量的实验,说明 神经网络的学习过程中,存在两个阶段:1)empirical error minimization (ERM) 2)representation compression。 可以这么来理解...
内容提示: Springer Nature 2021 LA T E X templateBlack-box Error Diagnosis in Deep Neural Networks forComputer Vision: a Survey of ToolsPiero Fraternali 1† , Federico Milani 1*† , Rocio Nahime Torres 1† and Niccol` oZangrando 1†1 Department of Electronics, Information and ...
Feature-Guided Black-Box Safety Testing of Deep Neural Networks 深度神经网络的功能导向黑盒安全测试 总结: 一种使用蒙特卡洛树搜索的方式来获取反例的方法,使用SIFT提取关键点,并采用两个玩家回合制的方式对关键点进行操作. 文章目录 Feature-Guided Black-Box Safety Testing of Deep Neu... ...
Black-box testingDeep Neural NetworkDeep Neural Networks (DNNs) are being used in various daily tasks such as object detection, speech processing, and machine translation. However, it is known that DNNs suffer from robustness problems -- perturbed inputs called adversarial samples leading to ...
In these types of watermarks, the technique typically involves embedding specially-crafted inputs into the training of a neural network designed to have a highly consistent, but unusual, output during testing. For example, a watermark may be embedded into a network by including a subset of ...
使用一类现实和自然纹理来生成对抗样本, 利用扰动对机器学习算法最终的结果产生不同. 程序化噪声广泛应用于计算机图形学, 并且在电影和视频游戏中有大量的应用, 用来生成仿真的纹理来细化自然的细节, 进而增强图像, 特别地, 比如玻璃, 树木, 大理石和动画(比如云, 火焰和波纹)的纹理. 由于这些特征, 假设程序化的噪...
Procedural Noise Adversarial Examples for Black-Box Attacks on Deep Neural Networks论文笔记 0. 概述 如今一些深度神经网络对于一些对抗性样本(Adversarial sample)是弱势的, 对抗性样本就是指我们对输入进行特定的改变, 通过原有的学习算法最终导致整个网络内部出现误差, 这属于攻击的一种, 然而, 现在的攻击都是要...
Despite their great success, there is still no comprehensive theoretical understanding of learning with Deep Neural Networks (DNNs) or their inner organization. Previous work proposed to analyze DNNs in the extit{Information Plane}; i.e., the plane of the Mutual Information values that each layer...