deep-residual-learning
There are 13 repositories under deep-residual-learning topic.
iduta/iresnet
Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)
shuokay/resnet
Deep Residual Learning for Image Recognition, http://arxiv.org/abs/1512.03385
dnlcrl/deep-residual-networks-pyfunt
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).
a-martyn/resnet
An implementation of the original "ResNet" paper in Pytorch
iduta/coconv
[ICCV W] Contextual Convolutional Neural Networks (https://arxiv.org/pdf/2108.07387.pdf)
KanishkNavale/DQN-DuelingDQN
Tensorflow based DQN and PyTorch based DDQN Agent for 'MountainCar-v0' openai-gym environment.
yixindu1573/Recursive-Deep-Residual-Learning-for-Single-Image-Dehazing-DRL
Recursive Deep Residual Learning for Single Image Dehazing (DRL)
Chandra-cc/IDCBreastCancer_histopathologyImages_deepResidualLearning
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.
Chandra-cc/Pneumonia-diagnosis-using-deep-residual-learning-with-a-classification-accuracy-of-98.22
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.
Ayush-Shirsat/EfficientNet_DR
CS 591 Deep learning Project
Chandra-cc/Object_Classification_Deep_Residual_Seperable_CNN_with_VGG16
Object_Classification_Deep_Residual_Seperable_CNN_with_VGG16
NatenaelTBekele/Facial-Expression-Emotion-Recognition-Model
The aim of this project is to classify people’s emotions based on their face images
vargo96/seminars
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.