Pinned Repositories
Automatic-Target-Classification-In-SAR-Images-Using-Convolutional-Neural-Networks
SAR -> Synthetic Aperture Radar. This project is based on predicting the accuracy of the testing data set over the training data set using the MSTAR(Moving and Stationary Target Acquisition and Recognition) database and plotting the graph of the Results.csv file.
DeepFool
A simple and accurate method to fool deep neural networks
FGSM
Simple pytorch implementation of FGSM and I-FGSM
GANforSAR
generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
GraphEmbedding
Implementation and experiments of graph embedding algorithms.
jupiter8
Synthesizing SAR images with GANs
ML-CV
机器学习实战
MSTAR
ATR
MSTAR-AConvNet
AConvNet on Caffe for [Chen et al. IEEE TGRS vol.54 no.8]
Lulu-gaga's Repositories
Lulu-gaga/Automatic-Target-Classification-In-SAR-Images-Using-Convolutional-Neural-Networks
SAR -> Synthetic Aperture Radar. This project is based on predicting the accuracy of the testing data set over the training data set using the MSTAR(Moving and Stationary Target Acquisition and Recognition) database and plotting the graph of the Results.csv file.
Lulu-gaga/DeepFool
A simple and accurate method to fool deep neural networks
Lulu-gaga/FGSM
Simple pytorch implementation of FGSM and I-FGSM
Lulu-gaga/GANforSAR
Lulu-gaga/generative-models
Collection of generative models, e.g. GAN, VAE in Pytorch and Tensorflow.
Lulu-gaga/GraphEmbedding
Implementation and experiments of graph embedding algorithms.
Lulu-gaga/jupiter8
Synthesizing SAR images with GANs
Lulu-gaga/ML-CV
机器学习实战
Lulu-gaga/MSTAR
ATR
Lulu-gaga/MSTAR-AConvNet
AConvNet on Caffe for [Chen et al. IEEE TGRS vol.54 no.8]
Lulu-gaga/mstar_with_machine_learning
Simple implementation of sar target recognition using machine learning methods
Lulu-gaga/universal
Lulu-gaga/Universal-Adversarial-Perturbation
This is PyTorch Implementation of Universal Adversarial Perturbation (https://arxiv.org/abs/1610.08401)