Shao-Wei Chiu's repositories
pytorch-cifar
95.47% on CIFAR10 with PyTorch
NYCU-Thesis-Template
A NYCU thesis template
Machine-Learning
2020 NCTU Machine Learning
SEAL-Python
Microsoft SEAL 3.X For Python
delphi
A Cryptographic Inference Service for Neural Networks
FaceX-Zoo
A PyTorch Toolbox for Face Recognition
facenet-pytorch-vggface2
A PyTorch implementation of the 'FaceNet' paper for training a facial recognition model with Triplet Loss using the VGGFace2 dataset. A pre-trained model using Triplet Loss is available for download.
nctu-thesis
NCTU Thesis Template in XeLaTeX
insightface
Face Analysis Project on MXNet and PyTorch
xfr
Explainable Face Recognition ECCV 2020 Paper code and dataset repository
nctu-thesis-latex
A LaTeX template for writing thesis in NCTU.
face-alignment
:fire: 2D and 3D Face alignment library build using pytorch
Accelerator-Architectures-for-Machine-Learning
2020 NCTU Accelerator Architectures for Machine Learning
googLeNet
The simple comparison between naive inception and inception v2 in gooLeNet
Unix-Programming
2020 NCTU Unix Programming
private-ai-resources
SOON TO BE DEPRECATED - Private machine learning progress
snake
snake game in C
fibdrv
Linux kernel module that calculates Fibonacci numbers
lab0-c
C Programming Lab: Assessing Your C Programming Skills
Game-Theory
Term-Project of Game Theory
CryptoNets
CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.
excitationbp
Visualizing how deep networks make decisions
server-framework
Asynchronous server framework in modern C