LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation, CVPR 2018 (Spotlight paper, 6.6%)

Optical Flow Prediction with TensorFlow. Implements "PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume," by Deqing Sun et al. (CVPR 2018)

A High-Quality Real Time Upscaler for Anime Video

Caffe implementation of "Fast and Accurate Single Image Super-Resolution via Information Distillation Network" (CVPR 2018)

An attempt at a PyTorch implimentation of "Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation"

PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume, CVPR 2018 (Oral)

TOFlow: Video Enhancement with Task-Oriented Flow

List of AI Residency & Research programs, Ph.D Fellowships, Research Internships

The py version of toflow → https://github.com/anchen1011/toflow

EnhanceNet: Single Image Super-Resolution Through Automated Texture Synthesis (official repository)

🗺️ Generate an interactive geo heatmap from your Google location data

Probabilistic time series modeling in Python

CryptoNets is a demonstration of the use of Neural-Networks over data encrypted with Homomorphic Encryption. Homomorphic Encryptions allow performing operations such as addition and multiplication over data while it is encrypted. Therefore, it allows keeping data private while outsourcing computation (see here and here for more about Homomorphic Encryptions and its applications). This project demonstrates the use of Homomorphic Encryption for outsourcing neural-network predictions. The scenario in mind is a provider that would like to provide Prediction as a Service (PaaS) but the data for which predictions are needed may be private. This may be the case in fields such as health or finance. By using CryptoNets, the user of the service can encrypt their data using Homomorphic Encryption and send only the encrypted message to the service provider. Since Homomorphic Encryptions allow the provider to operate on the data while it is encrypted, the provider can make predictions using a pre-trained Neural-Network while the data remains encrypted throughout the process and finaly send the prediction to the user who can decrypt the results. During the process the service provider does not learn anything about the data that was used, the prediction that was made or any intermediate result since everything is encrypted throughout the process. This project uses the Simple Encrypted Arithmetic Library SEAL version 3.2.1 implementation of Homomorphic Encryption developed in Microsoft Research.

Machine Learning inference engine for Microcontrollers and Embedded devices

A free and unlimited API for Google Translate 💵🚫

Client library for OAuth2/OpenID Connect

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.

Image, GIF and Video enlarger/upscaler achieved with waifu2x and Anime4K. [NO LONGER UPDATED]

👷‍♂️ A simple package for extracting useful features from character objects 👷‍♀️

🈯 A Node.JS library to consume Google Translate API for free.