Multi-Label Adversarial Perturbations 文章目录 Multi-Label Adversarial Perturbations ICDM2018 1.介绍 1.1多标签对抗样本广泛存在于现实生活中。 1.2生成多标签对抗样本的挑战。 1.3针对多标签攻击本文设计了2种框架4种方法 2.符号 2.1有目标攻击定义 3.多标签目标攻击 3.1 Classification-targeted Framework 3.1.1 ML...
\quad作者主要是将CW、DeepFool、FGSM和FGM转化为多标签的攻击。 \quadMulti-label Carlini & Wagner Attack (ML-CW): \quadMulti-label DeepFool Attack (ML-DP): \quadFGSM和FGM直接算loss反向即可。 \quad对于排序的情况,利用标签之间的关系,提出了2种攻击,Rank I和Rank II。 \quad定义\Omega^-=A_1 \...
Secondly, we comprehensively compared the performance of these five attack algorithms and the other four existing multi-label adversarial attack algorithms by experiments on six different attack types, and evaluated the transferability of adversarial examples generated by all algorithms under two attack ...
Due to the vulnerability of multi-focus image fusion models to adversarial perturbations, these models may make incorrect predictions about the decision maps guiding the fusion. If the fused image contains a large portion of non-focus areas, its anomalies become easily detectable. To address this i...
Third, two graph autoencoders (GAEs) with collaborative training are constructed to simulate label propagation to predict potential associations. On this basis, free multiscale adversarial training (FMAT) is introduced. FMAT enhances node feature qual- ity through small gradient adversarial...
a–d, UMAP representation computed from the latent space of MultiVI in which cells are color labeled by their modality (a) and cell-type label (b); scATAC-seq PBMC cells labeled by the replicate from which they were collected (c) and scRNA-seq cells labeled by their experimental technology...
However, supervised adversarial training has the disadvantage of label leakage, and PGD attack uses cross entropy loss for adversary generation, disregarding the original data manifold structure [19]. These result in the model to overfit on perturbations, thus affecting model generalisation [28], [21...
However, the label set of RCV1-2K has been expanded with some new labels. It contains2456 labels. RCV1是从1996-1997年的《路透社新闻》文章中收集的, 带有103个类别的人工标注标签。 它分别由23,149个训练和784,446个测试文本组成。 RCV1-2K数据集具有与RCV1相同的功能。 但是,RCV1-2K的标签集...
To improve the reliability of pseudo labels, one can adopt an iterative training procedure, which distills previously learned knowledge into a neural network with an equal or larger capacity to boost model performance on label estimation (Xie et al., 2020c; Zoph et al., 2020). Also, it is...
However, they face challenges such as label sensitivity, model structure, selection of model layers, and knowledge redundancy. Given the significant domain differences between natural and medical images, arbitrarily matching model layers for knowledge transfer may not benefit, and could even hinder, ...