mengzifds's repositories
albumentations
Fast image augmentation library and easy to use wrapper around other libraries. Documentation: https://albumentations.ai/docs/ Paper about library: https://www.mdpi.com/2078-2489/11/2/125
contrastive-unpaired-translation
Contrastive unpaired image-to-image translation, faster and lighter training than cyclegan (ECCV 2020, in PyTorch)
CycleGAN-2
CycleGAN Tensorflow Implementation
CycleGAN-Tensorflow-2
CycleGAN Tensorflow 2
DALL-E
PyTorch package for the discrete VAE used for DALL·E.
deep_learning_object_detection
A paper list of object detection using deep learning.
DeepCTR
Easy-to-use,Modular and Extendible package of deep-learning based CTR models for search and recommendation.
DeepCTR-Torch
【PyTorch】Easy-to-use,Modular and Extendible package of deep-learning based CTR models.
EfficientNet-PyTorch
A PyTorch implementation of EfficientNet
gpt-3
GPT-3: Language Models are Few-Shot Learners
GraphEmbedding
Implementation and experiments of graph embedding algorithms.
imgaug
Image augmentation for machine learning experiments.
jukebox
Code for the paper "Jukebox: A Generative Model for Music"
librec
LibRec: A Leading Java Library for Recommender Systems, see
lightfm
A Python implementation of LightFM, a hybrid recommendation algorithm.
magenta
Magenta: Music and Art Generation with Machine Intelligence
mmdetection
OpenMMLab Detection Toolbox and Benchmark
pytorch-CycleGAN-and-pix2pix
Image-to-Image Translation in PyTorch
reinforcement-learning-an-introduction
Python Implementation of Reinforcement Learning: An Introduction
spleeter
Deezer source separation library including pretrained models.
torchxrayvision
TorchXRayVision: A library of chest X-ray datasets and models.
transformers
🤗Transformers: State-of-the-art Natural Language Processing for Pytorch and TensorFlow 2.0.
U-Time
U-Time: A Fully Convolutional Network for Time Series Segmentation
unet
Generic U-Net Tensorflow 2 implementation for semantic segmentation
vdvae
Repository for the paper "Very Deep VAEs Generalize Autoregressive Models and Can Outperform Them on Images"