The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory.
AMC-GNN generates two graph views by data augmentation and compares different layers' output embeddings of Graph Neural Network encoders to obtain feature representations, which could be used for downstream tasks.
In this paper, we propose a new gradient iteration framework, which redefines the relationship between the above three.
Traditional methods such as resampling, reweighting, and synthetic samples that deal with imbalanced datasets are no longer applicable in GNN.
Experimentally, we show that our ASR of adversarial attack reaches to 58. 38% on average, which outperforms the state-of-the-art method by 12. 1% on the normally trained models and by 11. 13% on the adversarially trained models.
Except for deep network structure, the task or corresponding big dataset is also important for deep network models, but neglected by previous studies.
In this study, we proposed a new GAN-based Bayesian visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data, a pre-trained conditional generator to generate natural images of specified categories, and a set of encoding models and evaluator to evaluate generated images.
To alleviate the tradeoff between the attack success rate and image fidelity, we propose a method named AdvJND, adding visual model coefficients, just noticeable difference coefficients, in the constraint of a distortion function when generating adversarial examples.
The proposed model comprising a texture transfer network (TTN) and an auxiliary defense generative adversarial networks (GAN) is called Human-perception Auxiliary Defense GAN (HAD-GAN).
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation.
In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them.
Despite the hierarchically similar representations of deep network and human vision, visual information flows from primary visual cortices to high visual cortices and vice versa based on the bottom-up and top-down manners, respectively.
Neurons and Cognition
In this framework, we employ the transfer learning technique to incorporate a pre-trained DNN (i. e., AlexNet) and train a nonlinear mapping from visual features to brain activity.
Without semantic prior information, we present a novel method to reconstruct nature images from fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN).
We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from high-level capsule features to image stimuli again in an end-to-end manner.
The qualitative and quantitative evaluations of experimental results indicate that the proposed method show a stable and prospective performance on artifacts reduction and detail recovery for limited angle tomography.