Reference code for the paper: Deep White-Balance Editing (CVPR 2020). Our method is a deep learning multi-task framework for white-balance editing.

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation net…

Pytorch code for ICRA'22 paper: "Single-Shot Multi-Object 3D Shape Reconstruction and Categorical 6D Pose and Size Estimation"

This project proposes an end-to-end framework for semi-supervised Anomaly Detection and Segmentation in images based on Deep Learning.

Anomaly detection tutorial on univariate time series with an auto-encoder

Experiments on unsupervised point cloud reconstruction.

CAE-LO: LiDAR Odometry Leveraging Fully Unsupervised Convolutional Auto-Encoder for Interest Point Detection and Feature Description

Tensorflow implementation of Semi-supervised Sequence Learning (https://arxiv.org/abs/1511.01432)

Implementation of a 3D Face Generative Model

由时间空间成对组成的轨迹序列，通过循环神经网络lstm，自编码器auto-encode，时空密度聚类st-dbscan做异常检测

Face enhancer‏ - Denoising Auto Encoder by Tensorflow and Keras and skimage

Custom PyTorch model (VGG-16 Auto-Encoder) and custom criterion (Local Aggregation) for image clustering. The repo contains elaborated creation of fungi image data using factory method.

Drug Similarity Integration Through Attentive Multi-view Graph Auto-Encoders (IJCAI 2018)

Attention Deeplabv3+: Multi-level Context Attention Mechanism for Skin Lesion Segmentation

[WSDM 2024] GAD-NR : Graph Anomaly Detection via Neighborhood Reconstruction

A system that will help in a personalized recommendation of courses for an upcoming semester based on the performance of previous semesters.

Implementation of 'Self-Adversarial Variational Autoencoder with Gaussian Anomaly Prior Distribution for Anomaly Detection'

Learning Embedding of 3D models with Quadric Loss

The unsupervised learning problem trains a diffeomorphic spatio-temporal grid, that registers the output sequence of the PDEs onto a non-uniform parameter/time-varying grid, such that the Kolmogorov n-width of the mapped data on the learned grid is minimized.

2020 Spring Fudan University Machine Learning Course HW by prof. Chen Qin. 复旦大学大数据学院2020年春季课程-人工智能与机器学习（DATA620006）