Pinned Repositories
12
biggan
BigGAN-PyTorch
See https://github.com/ilyakava/gan for results on Imagenet 128. Code for a Multi-Hinge Loss with K+1 Conditional GANs
clever
CLEVER-Robustness-Score
Codes for reproducing the robustness evaluation scores in “Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” ICLR 2018
GREAT-Score
GREAT_SCORE
learn_python3_spider
python爬虫教程系列、从0到1学习python爬虫，包括浏览器抓包，手机APP抓包，如 fiddler、mitmproxy，各种爬虫涉及的模块的使用，如：requests、beautifulSoup、selenium、appium、scrapy等，以及IP代理，验证码识别，Mysql，MongoDB数据库的python使用，多线程多进程爬虫的使用，css 爬虫加密逆向破解，JS爬虫逆向，分布式爬虫，爬虫项目实战实例等
lizaitang.github.io
PyTorch-StudioGAN
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
lizaitang's Repositories
lizaitang/12
lizaitang/biggan
lizaitang/BigGAN-PyTorch
See https://github.com/ilyakava/gan for results on Imagenet 128. Code for a Multi-Hinge Loss with K+1 Conditional GANs
lizaitang/clever
lizaitang/CLEVER-Robustness-Score
Codes for reproducing the robustness evaluation scores in “Evaluating the Robustness of Neural Networks: An Extreme Value Theory Approach,” ICLR 2018
lizaitang/GREAT-Score
lizaitang/GREAT_SCORE
lizaitang/learn_python3_spider
python爬虫教程系列、从0到1学习python爬虫，包括浏览器抓包，手机APP抓包，如 fiddler、mitmproxy，各种爬虫涉及的模块的使用，如：requests、beautifulSoup、selenium、appium、scrapy等，以及IP代理，验证码识别，Mysql，MongoDB数据库的python使用，多线程多进程爬虫的使用，css 爬虫加密逆向破解，JS爬虫逆向，分布式爬虫，爬虫项目实战实例等
lizaitang/lizaitang.github.io
lizaitang/PyTorch-StudioGAN
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation.
lizaitang/adversarial-robustness-toolbox
Adversarial Robustness Toolbox (ART) - Python Library for Machine Learning Security - Evasion, Poisoning, Extraction, Inference - Red and Blue Teams
lizaitang/RESIDE
EMNLP 2018: RESIDE: Improving Distantly-Supervised Neural Relation Extraction using Side Information
lizaitang/WordGCN
ACL 2019: Incorporating Syntactic and Semantic Information in Word Embeddings using Graph Convolutional Networks