焓韡's repositories
Adversarial-Reading
Paper sharing in adversary related works
walking-on-the-edge-fast-low-distortion-adversarial-examples
Walking on the Edge: Fast, Low-Distortion Adversarial Examples
SmoothAdversarialExamples
Smooth Adversarial Examples
Interpretability-methods-for-self-supervised-and-supervised-models
In recent years, the rapid development of Deep Neural Networks (DNN) has led to a remarkable performance in many complex tasks in the field of computer vision at the cost of the models’ complexity. The more complex the models get, the higher the need is for understanding them. The primary objective of this repo is to give visual explanations on what both supervised and self-supervised methods really learn during training. Self-supervised and supervised state-of-the-art pre-trained models will be investigated. As backbone networks, for both categories convnets and Transformers based architectures will be used. Variation of visualization techniques will be used.
Tree-structured-decomposition-and-adaptation-in-moea-d
Tree-structured decomposition and adaptation in moea/d
Adversarial-Training-for-Free
Unofficial implementation of the paper 'Adversarial Training for Free'
dino
PyTorch code for Vision Transformers training with the Self-Supervised learning method DINO
foolbox
Python toolbox to create adversarial examples that fool neural networks in PyTorch, TensorFlow, Keras, …
hanwei0912.github.io
Hanwei | Homepage
jMetalPy
A framework for single/multi-objective optimization with metaheuristics
knn-defense
Defending Against Adversarial Examples with K-Nearest Neighbor
MathSTIC-UBL
Doctoral Thesis Class for the MathSTIC Doctoral School / Université Bretagne Loire
Quantus
Quantus is an eXplainable AI toolkit for responsible evaluation of neural network explanations