Liu Yiwen's repositories
BBS-be
NCU Java Web
books
"我的阅历"
COMP_5331_Project_Fake_News_Detection
- Image classification using Deep learning. - Utilizing both frequency and pixel domain information of images. - Implemented MVNN model from a research paper published in 2019 IEEE ICDM. Achieved comparable accuracy to the original model. - Compared with several deep learning models: Pre-trained (VGG-16, VGG-19) and our own implementations of CNN based models (with different number of layers) - Research project utilized as a part of a course: COMP 5331 - Knowledge Discovery in Databases, from HKUST (The Hong Kong University of Science and Technology). - Referenced paper (for MVNN model) - P. Qi, J. Cao, T. Yang, J. Guo, and J. Li. Exploiting multi-domain visual information for fake news detection. In 2019 IEEE International Conference on Data Mining (ICDM), pages 518–527, 2019.
deep-image-prior
Image restoration with neural networks but without learning.
DeepLearning
深度学习入门教程, 优秀文章, Deep Learning Tutorial
eoj3
华东师范大学在线测评系统。https://acm.ecnu.edu.cn/ https://eoj.i64d.com/
expert_readed_books
2021年最新总结，推荐工程师合适读本，计算机科学，软件技术，创业，**类，数学类，人物传记书籍
gpt2-ml
GPT2 for Multiple Languages, including pretrained models. GPT2 多语言支持, 15亿参数中文预训练模型
img2latex-mathpix
An image to LaTeX tool by MathpixOCR API and JavaFX
LeetcodeSolutions
Leetcode Solutions
ML-NLP
此项目是机器学习(Machine Learning)、深度学习(Deep Learning)、NLP面试中常考到的知识点和代码实现，也是作为一个算法工程师必会的理论基础知识。
n2n-watermark-remove
基于noise2noise修改的深度学习去水印项目。
noise2noise
An unofficial and partial Keras implementation of "Noise2Noise: Learning Image Restoration without Clean Data"
pix2pix-tensorflow
TensorFlow implementation of "Image-to-Image Translation Using Conditional Adversarial Networks".
Python-Foundation-Suda
苏州大学计算机复试上机2005-2020真题、保研题、期中期末习题、苏大mooc老师推荐力扣习题
Python-Foundation-Suda-1
Python Foundation; Soochow University Python; 苏州大学复试上机
pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch
PyTorch-GAN
PyTorch implementations of Generative Adversarial Networks.
reproducible-image-denoising-state-of-the-art
Collection of popular and reproducible image denoising works.