Pinned Repositories
admm-pruning
Prune DNN using Alternating Direction Method of Multipliers (ADMM)
Adversarial-Attacks-and-Defences
A defense algorithm which utilizes the combination of an auto- encoder and block-switching architecture. Auto-coder is intended to remove any perturbations found in input images whereas block switching method is used to make it more robust against White-box attack. Attack is planned using FGSM model, and the subsequent counter-attack by the proposed architecture will take place thereby demonstrating the feasibility and security delivered by the algorithm.
adversarial-attacks-pytorch
PyTorch implementation of adversarial attacks.
adversarial-robustness-toolbox
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
adversarial_ecoc_lwta
Local Competition and Uncertainty for Adversarial Robustness
AdversarialQuerying
A PyTorch implementation of the method found in "Adversarially Robust Few-Shot Learning: A Meta-Learning Approach"
annotated_deep_learning_paper_implementations
🧑🏫 50! Implementations/tutorials of deep learning papers with side-by-side notes 📝; including transformers (original, xl, switch, feedback, vit, ...), optimizers (adam, adabelief, ...), gans(cyclegan, stylegan2, ...), 🎮 reinforcement learning (ppo, dqn), capsnet, distillation, ... 🧠
awesome-few-shot-learning
A review for latest few-shot learning works
awesome-real-world-adversarial-examples
😎 A curated list of awesome real-world adversarial examples resources
CloserLookFewShot
source code to ICLR'19, 'A Closer Look at Few-shot Classification'
WangFP-516's Repositories
WangFP-516/ROBY-Evaluating-the-Robustness-of-a-Deep-Model-by-its-Decision-Boundaries
WangFP-516/adversarial_ecoc_lwta
Local Competition and Uncertainty for Adversarial Robustness
WangFP-516/pytorch-handbook
pytorch handbook是一本开源的书籍，目标是帮助那些希望和使用PyTorch进行深度学习开发和研究的朋友快速入门，其中包含的Pytorch教程全部通过测试保证可以成功运行
WangFP-516/admm-pruning
Prune DNN using Alternating Direction Method of Multipliers (ADMM)
WangFP-516/latexify_py
Generates LaTeX math description from Python functions.
WangFP-516/AdversarialQuerying
A PyTorch implementation of the method found in "Adversarially Robust Few-Shot Learning: A Meta-Learning Approach"
WangFP-516/DN4
The Pytorch code of "Revisiting Local Descriptor based Image-to-Class Measure for Few-shot Learning", CVPR 2019.
WangFP-516/Detection-PyTorch-Notebook
代码 -《深度学习之PyTorch物体检测实战》
WangFP-516/fewshotDatasetDesign
The paper studies the problem of learning to recognize a new class of objects from a very small number of labeled images. This is called few-shot learning. Previous work in the literature focused on designing new algorithms that allow to learn to generalize to new unseen classes.In this work, we consider the impact of the dataset that we train on, and experiment with some dataset manipulations to see which trade-offs are important in the design of a dataset aimed at few-shot learning.
WangFP-516/awesome-real-world-adversarial-examples
😎 A curated list of awesome real-world adversarial examples resources
WangFP-516/m_testing_adversatial_sample
WangFP-516/CNN_MaxPooling
WangFP-516/few-shot
Repository for few-shot learning machine learning projects
WangFP-516/deepxplore
DeepXplore code release
WangFP-516/MetaOptNet
Meta-Learning with Differentiable Convex Optimization (CVPR 2019 Oral)
WangFP-516/CVPR_2019_PNI
pytorch implementation of Parametric Noise Injection for adversarial defense
WangFP-516/soft-filter-pruning
Soft Filter Pruning for Accelerating Deep Convolutional Neural Networks
WangFP-516/ordinal-pooling-layers
WangFP-516/deepgini
WangFP-516/FewShotWithoutForgetting
WangFP-516/sppnet-pytorch
A simple Spatial Pyramid Pooling layer which could be added in CNN
WangFP-516/Pytorch-STL10
WangFP-516/explanatoryGraph
Interpreting CNN Knowledge via an Explanatory Graph
WangFP-516/MAML-TensorFlow-1
Faster and elegant TensorFlow Implementation of paper: Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
WangFP-516/s3pool