Jahan's Projects
The authors official implementation of Unsupervised Discovery of Interpretable Directions in the GAN Latent Space
Curated list of awesome GAN applications and demo
Generative Flow Networks
Grounded Language-Image Pre-training
Gluon CV Toolkit
Probabilistic time series modeling in Python
Google Research
This is the official PyTorch implementation of our paper "Grad-PU: Arbitrary-Scale Point Cloud Upsampling via Gradient Descent with Learned Distance Functions" (CVPR 2023).
gradslam is an open source differentiable dense SLAM library for PyTorch
Algorithm & Data structure
Graph Classification with Graph Convolutional Networks in PyTorch (NeurIPS 2018 Workshop)
This is official Pytorch implementation of "Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks", Raghavendra Selvan et al. 2020
A General Simultaneous Localization and Mapping Framework which supports feature based or direct method and different sensors including monocular camera, RGB-D sensors or any other input types can be handled.
Master Reinforcement and Deep Reinforcement Learning using OpenAI Gym and TensorFlow
CVPR2023 - Activating More Pixels in Image Super-Resolution Transformer
The hector_localization stack is a collection of packages, that provide the full 6DOF pose of a robot or platform.
PyTorch deep learning models for document classification
Pytorch implementation of HighRes-net, a neural network for multi-frame super-resolution, trained and tested on the European Space Agencyβs Kelvin competition.
Hidden Markov Models in Python, with scikit-learn like API
[ICPR 2020] "Neural Compression and Filtering for Edge-assisted Real-time Object Detection in Challenged Networks" and [ACM MobiCom EMDL 2020] "Split Computing for Complex Object Detectors: Challenges and Preliminary Results"
HRViT ("Multi-Scale High-Resolution Vision Transformer for Semantic Segmentation"), CVPR 2022.
Optimized 3D CNN model for Human activity recognition
[ICML'21 Oral] I-BERT: Integer-only BERT Quantization
Code for reproducing experiments in "Improved Training of Wasserstein GANs"