ArnavVarma
Follow
Stars
A curated list of papers of interesting empirical study and insight on deep learning. Continually updating...
A collection of learning resources for curious software engineers
Code for WACV'24 paper Continual Learning of Unsupervised Monocular Depth from Videos
A Collection of LiDAR-Camera-Calibration Papers, Toolboxes and Notes
An Invitation to 3D Vision: A Tutorial for Everyone
You like pytorch? You like micrograd? You love tinygrad! ❤️
The official PyTorch code for ICLR'22 Paper "Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System""
This is the official repo for the CVPR 2021 IMW paper: "Perceptual Loss for Robust Unsupervised Homography Estimation"
The simplest, fastest repository for training/finetuning medium-sized GPTs.
Deep Learning papers reading roadmap for anyone who are eager to learn this amazing tech!
Convert Machine Learning Code Between Frameworks
Detailed and tailored guide for undergraduate students or anybody want to dig deep into the field of AI with solid foundation.
Official repo for Adversarial Attacks on Monocular Pose Estimation
All the hipster things in Neural Net in a single repo
A technical report on convolution arithmetic in the context of deep learning
Relevant papers in Continual Learning
Continual Learning papers list, curated by ContinualAI
A collection of educational notebooks on multi-view geometry and computer vision.
The AdLeap-MAS project proposes a new framework for implementing and simulating Ad-hoc reasoning environments. Built 100% in Python using the Open-AI Gym as its basis, we propose a component-based …
This is a toolbox repository to help evaluate various methods that perform image matching from a pair of images.
A collection of research materials on explainable AI/ML
this repository accompanies the book "Grokking Deep Learning"
ROADMAP(Mind Map) and KEYWORD for students those who have interest in learning NLP
Implementation of Reinforcement Learning Algorithms. Python, OpenAI Gym, Tensorflow. Exercises and Solutions to accompany Sutton's Book and David Silver's course.
Best Practices, code samples, and documentation for Computer Vision.