Object_Classification_Deep_Residual_Seperable_CNN_with_VGG16
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Updated
Nov 16, 2018 - Jupyter Notebook
Object_Classification_Deep_Residual_Seperable_CNN_with_VGG16
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.
CS 591 Deep learning Project
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.
Tensorflow based DQN and PyTorch based DDQN Agent for 'MountainCar-v0' openai-gym environment.
[ICCV W] Contextual Convolutional Neural Networks (https://arxiv.org/pdf/2108.07387.pdf)
Recursive Deep Residual Learning for Single Image Dehazing (DRL)
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.
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
Deep Residual Learning for Image Recognition, http://arxiv.org/abs/1512.03385
Improved Residual Networks (https://arxiv.org/pdf/2004.04989.pdf)
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