Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)

Deep Residual Learning for Image Recognition, http://arxiv.org/abs/1512.03385

Python implementation of "Deep Residual Learning for Image Recognition" (http://arxiv.org/abs/1512.03385 - MSRA, winner team of the 2015 ILSVRC and COCO challenges).

An implementation of the original "ResNet" paper in Pytorch

[ICCV W] Contextual Convolutional Neural Networks (https://arxiv.org/pdf/2108.07387.pdf)

Tensorflow based DQN and PyTorch based DDQN Agent for 'MountainCar-v0' openai-gym environment.

Recursive Deep Residual Learning for Single Image Dehazing (DRL)

IDC prediction in breast cancer histopathology images using deep residual learning with an accuracy of 99.37% in a subset of images containing a total of 7,500 microscopic images.

Classification between normal and pneumonia affected chest-X-ray images using deep residual learning along with separable convolutional network(CNN). This methodology involves efficient edge preservation and image contrast enhancement techniques for better classification of the X-ray images.

CS 591 Deep learning Project

PyTorch implementation of the CIFAR-10 ResNet models published in ""Deep Residual Learning for Image Recognition" (He et al. 2015)

Object_Classification_Deep_Residual_Seperable_CNN_with_VGG16

This repository contains my seminar work (literature review) for topics in Machine Learning, Pattern Recognition at Paderborn University. Each topic is in a separate folder and the folder name is the topic of my seminar work.

The aim of this project is to classify people’s emotions based on their face images