bellenliu

bellenliu's Stars

• junyanz/pytorch-CycleGAN-and-pix2pix

  Image-to-Image Translation in PyTorch

  Language:Python23.8k3441.5k6.4k

• AlexeyAB/darknet

  YOLOv4 / Scaled-YOLOv4 / YOLO - Neural Networks for Object Detection (Windows and Linux version of Darknet )

  Language:C22k4828.3k8k

• yzhao062/anomaly-detection-resources

  Anomaly detection related books, papers, videos, and toolboxes

  Language:Python8.6k285221.8k

• poloclub/cnn-explainer

  Learning Convolutional Neural Networks with Interactive Visualization.

  Language:JavaScript8.4k164501.3k

• XPixelGroup/BasicSR

  Open Source Image and Video Restoration Toolbox for Super-resolution, Denoise, Deblurring, etc. Currently, it includes EDSR, RCAN, SRResNet, SRGAN, ESRGAN, EDVR, BasicVSR, SwinIR, ECBSR, etc. Also support StyleGAN2, DFDNet.

  Language:Python7.3k875791.3k

• xinntao/ESRGAN

  ECCV18 Workshops - Enhanced SRGAN. Champion PIRM Challenge on Perceptual Super-Resolution. The training codes are in BasicSR.

  Language:Python6.3k1201321.1k

• podgorskiy/ALAE

  [CVPR2020] Adversarial Latent Autoencoders

  Language:Python3.5k8269556

• foolwood/SiamMask

  [CVPR19/TPAMI23] SiamMask: A Framework for Fast Online Object Tracking and Segmentation

  Language:Python3.5k94202810

• Machine-Learning-Tokyo/Interactive_Tools

  Interactive Tools for Machine Learning, Deep Learning and Math

  2.7k841317

• mit-han-lab/gan-compression

  [CVPR 2020] GAN Compression: Efficient Architectures for Interactive Conditional GANs

  Language:Python1.1k27101152

• samet-akcay/ganomaly

  GANomaly: Semi-Supervised Anomaly Detection via Adversarial Training

  Language:Python8682384213

• switchablenorms/DeepFashion_Try_On

  Official code for "Towards Photo-Realistic Virtual Try-On by Adaptively Generating↔Preserving Image Content"，CVPR‘20 https://arxiv.org/abs/2003.05863

  Language:Python8563399252

• Eatzhy/surface-defect-detection

  缺陷检测文献记录

  7722511180

• ShuaiLYU/Deep-Learning-Approach-for-Surface-Defect-Detection

  A Tensorflow implementation of "Segmentation-Based Deep-Learning Approach for Surface-Defect Detection"

  Language:Python7442543261

• xthan/VITON

  Code and dataset for paper "VITON: An Image-based Virtual Try-on Network"

  Language:Python5512255145

• sergeywong/cp-vton

  Reimplemented code for "Toward Characteristic-Preserving Image-based Virtual Try-On Network"

  Language:Python5081845183

• houssamzenati/Efficient-GAN-Anomaly-Detection

  Language:Python4351921137

• danieltan07/dagmm

  My attempt at reproducing the paper Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection

  Language:Jupyter Notebook407925108

• b01902041/Deep-Virtual-Try-on-with-Clothes-Transform

  An image-based virtual try-on system with deep learning.

  Language:Python4061610120

• LeeDoYup/AnoGAN-tf

  Unofficial Tensorflow Implementation of AnoGAN (Anomaly GAN)

  Language:Python27884489

• JDAI-CV/Down-to-the-Last-Detail-Virtual-Try-on-with-Detail-Carving

  Virtural try-on under arbitrary poses

  Language:Python21894866

• kensakurada/sscdnet

  Semantic Scene Change Detection Network (CSCDNet + SSCDNet)

  Language:Python10761523

• shionhonda/viton-gan

  Original implementation of the paper "VITON-GAN: Virtual Try-on Image Generator Trained with Adversarial Loss" by Shion Honda.

  Language:Python10581231

• zimenglan-sysu-512/deep-flow

  Language:C++712633

• cheapthrillandwine/Improving_Unsupervised_Defect_Segmentation

  This is Keras code from "Improving Unsupervised Defect Segmentation by Applying Structural Similarity to Autoencoders".

  Language:Jupyter Notebook534416

• phuccuongngo99/Fence_GAN

  Fence GAN: Towards Better Anomaly Detection

  Language:Python394213

• witnessai/LAPGAN

  Unofficial PyTorch implementation of LAPGAN (Deep Generative Image Models using a Laplacian Pyramid of Adversarial Networks, NIPS 2015)

  Language:Python13139

• amandaberg/GANanomalyDetection

  Language:Python12233

• Dai7Igarashi/Anomaly-Detection

  3102

• bellenliu/uav-simulator

  A Simulink-based simulator to study different aspects of autonomous fixed-wing aerial vehicles, such as the kinematics & dynamics, control, state estimation and path planning.

  Language:MATLAB1000