Beginner Level Deep Learning Tutorials in Pytorch with Youtube Videos!

Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"

Siamese and triplet networks with online pair/triplet mining in PyTorch

PyTorch implementation of Non-Local Neural Networks (https://arxiv.org/pdf/1711.07971.pdf)

TensorFlow and PyTorch implementation of "Meta-Transfer Learning for Few-Shot Learning" (CVPR2019)

(NeurIPS 2021) Realistic Evaluation of Transductive Few-Shot Learning

A Faster Pytorch Implementation of Multi-Head Self-Attention

multi instance attention network for few-shot learning

Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021

This repository contains the code used in my graduation project

Solutions for Codility puzzles in Python

Homework #1 for EE898 (Advanced Topics in Deep Learning for Robotics and Computer Vision)

Tools for generating mini-ImageNet dataset and processing batches

Code of Cross Attention Network for Few-shot Classification (NeurIPS 2019).

Examples of playing with Circle Loss from the paper "Circle Loss: A Unified Perspective of Pair Similarity Optimization", CVPR 2020.

Pytorch implementation of the paper "Circle Loss: A Unified Perspective of Pair Similarity Optimization"

A re-implementation of "Prototypical Networks for Few-shot Learning"

Code for "Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks"

Repository for few-shot learning machine learning projects

PyTorch code for CVPR 2018 paper: Learning to Compare: Relation Network for Few-Shot Learning (Few-Shot Learning part)

Elegant PyTorch implementation of paper Model-Agnostic Meta-Learning (MAML)

Code for ICLR19 paper: Learning to Propagate Labels: Transductive Propagation Network for Few-shot Learning.

Code for reproducing the results of our "In Defense of the Triplet Loss for Person Re-Identification" paper.

Multi-head attention in PyTorch

Multi heads attention for image classification

Pytorch implementation of the paper "Optimization as a Model for Few-Shot Learning"