clean_image_classifier Submit to github! May 8, 2020 dataset commit of MetaAdvDet Oct 28, 2019 deep_learning_adv_detector commit of MetaAdvDet Oct 28, 2019 draw_figure ACM MultiMedia 2019 投稿代码 May 16, 2019 evaluation_toolkit commit of MetaAdvDet Oct 28, 2019 ...
classDualObject(nn.Module,metaclass=ABCMeta):@abstractmethoddef__init__(self):""" Initialize a dual layer by initializing the variables needed tocompute this layer's contribution to the upper and lower bounds.In the paper, if this object is at layer i, this is initializing `h'with the req...
Moreover, to fast update the generator with a few observations, the entire adversarial framework is learned in the gradient-based meta-learning manner. The experimental results on AI2THOR and RoboTHOR simulators demonstrate the effectiveness of the proposed method in navigating to unseen object ...
本文介绍图对抗攻击相关的三篇文章:Adversarial Attacks on Neural Networks for Graph Data[1]、 Adversarial Attacks on Graph Neural Networks via Meta Learning[2]和Topology Attack and Defense for Graph Neural Networks: An Optimization Perspective[3]。 一、图对抗攻击 由于深度神经网络强大的表示学习能力,近...
1. 论文相关 NeurIPS 2018 2.摘要 2.1 摘要 在这篇文章中,我们提出了一个概念简单且通用的框架,称为MetaGAN,用于解决小样本学习问题。大多数最先进的...
Compound domain generalization via meta- knowledge encoding. In CVPR, 2022. [7] Ting Chen, Xiaohua Zhai, Marvin Ritter, Mario Lucic, and Neil Houlsby. Self-supervised gans via auxiliary rotation loss. In CVPR, 2019. [8] Franc¸ois Chollet....
In the training phase, we first locate the generalization problem to the visual perception module, and then compare two meta-learning algorithms for better generalization in seen and unseen environments. One of them uses the Model-Agnostic Meta-Learning (MAML) algorithm that requires a few shot ...
对抗训练(adversarial training)是增强神经网络鲁棒性的重要方式。在对抗训练的过程中,样本会被混合一些微小的扰动(改变很小,但是很可能造成误分类),然后使神经网络适应这种改变,从而对对抗样本具有鲁棒性。 在图像领域,采用对抗训练通常能提高鲁棒性,但是通常都会造成泛化性降低,也就是说,虽然对对抗样本的抵抗力提升了...
2020A Survey of Privacy Attacks in Machine Learning 2020Learning from Noisy Labels with Deep Neural Networks: A Survey 2020Optimization for Deep Learning: An Overview 2020Backdoor Attacks and Countermeasures on Deep Learning: A Comprehensive Review ...
$ python .\LURE_main.py --data <data_dir> --log-dir <log_dir> --run <name_of_the_experiment> --dataset cifar10 --arch resnet18 \ --seed 10 --epochs 50 --decreasing_lr 20,40 --batch_size 64 --weight_decay 1e-4 --meta_batch_size 6250 --meta_batch_number 8 --snip_...