Domain Adaptation with Adversarial Training and Graph Embeddings
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Updated
Aug 14, 2018 - Python
Domain Adaptation with Adversarial Training and Graph Embeddings
Tensorflow deep learning based domain adaptation model implementations with experiment of estimate MNIST by SVHN data (SVHN -> MNIST): DANN (domain-adversarial neural network), Deep JDOT (joint distribution optimal transportation)
Experiments with distributionally robust optimization (DRO) for deep neural networks
A short literature review on how neural networks are easily fooled.
Chapter 11: Transfer Learning/Domain Adaptation
[NAACL 2018] Robust Sequence Labeling with Adversarial Training
Retinal lesions segmentation using CNNs and adversarial training: A Degree Thesis Submitted to the Faculty of Escola Tècnica d’Enginyeria de Telecomunicació de Barcelona of Universitat Politècnica de Catalunya.
Ensemble Adversarial Black-Box Attacks against Deep Learning Systems Trained by MNIST, USPS and GTSRB Datasets
some paper of Knowledge Distillation and Adversarial Training about NLP
Code for the paper "Addressing Model Vulnerability to Distributional Shifts over Image Transformation Sets", ICCV 2019
Migrate to PyTorch. Re-implementation of Bayesian Convolutional Neural Networks (BCNNs)
Code for the paper "MMA Training: Direct Input Space Margin Maximization through Adversarial Training"
Implementation of adversarial training under fast-gradient sign method (FGSM), projected gradient descent (PGD) and CW using Wide-ResNet-28-10 on cifar-10. Sample code is re-usable despite changing the model or dataset.
Pytorch-Named-Entity-Recognition-with-transformers
Analyzing Conditional Adversarial Networks to solve image recovery problems like shadow recovery, denoising and deblurring - CVIP 2019
Chainer implementation of Bayesian Convolutional Neural Networks (BCNNs)
WideResNet implementation on MNIST dataset. FGSM and PGD adversarial attacks on standard training, PGD adversarial training, and Feature Scattering adversarial training.
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