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.

Object_Classification_Deep_Residual_Seperable_CNN_with_VGG16

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)

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

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.