Distinguishing Commercial from Editorial Content in News

Overview

Distinguishing Commercial from Editorial Content in News License: MIT

In this repository you can find the following:

  • An anonymized version of the data used for my thesis. Due to the legislation around intellectual property it's not allowed to publish full articles. Therefore the data set only shows the title, the news source and the sponsor (if any).
  • The lexicon that was derived from this research. Contains 5000 terms with catagory and score.
  • Some source code that was used. If you download it the machine learning won't work since it requires the full/non-anonymized data set.
  • Game.py, which is a small quiz game that tests whether you can differentiate articles and advertorials!

Other sources related to this research:

Owner
Timo Kats
Computer Science and Economics student at Leiden University
Timo Kats
Allows including an action inside another action (by preprocessing the Yaml file). This is how composite actions should have worked.

actions-includes Allows including an action inside another action (by preprocessing the Yaml file). Instead of using uses or run in your action step,

Tim Ansell 70 Nov 04, 2022
A PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detection.

R-YOLOv4 This is a PyTorch-based R-YOLOv4 implementation which combines YOLOv4 model and loss function from R3Det for arbitrary oriented object detect

94 Dec 03, 2022
Spatial-Location-Constraint-Prototype-Loss-for-Open-Set-Recognition

Spatial Location Constraint Prototype Loss for Open Set Recognition Official PyTorch implementation of "Spatial Location Constraint Prototype Loss for

Xia Ziheng 12 Jun 24, 2022
This is the latest version of the PULP SDK

PULP-SDK This is the latest version of the PULP SDK, which is under active development. The previous (now legacy) version, which is no longer supporte

78 Dec 07, 2022
The Pytorch implementation for "Video-Text Pre-training with Learned Regions"

Region_Learner The Pytorch implementation for "Video-Text Pre-training with Learned Regions" (arxiv) We are still cleaning up the code further and pre

Rui Yan 0 Mar 20, 2022
Half Instance Normalization Network for Image Restoration

HINet Half Instance Normalization Network for Image Restoration, based on https://github.com/megvii-model/HINet. Dependencies NumPy PyTorch, preferabl

Holy Wu 4 Jun 06, 2022
Image Processing, Image Smoothing, Edge Detection and Transforms

opevcvdl-hw1 This project uses openCV and Qt to achieve the requirements. Version Python 3.7 opencv-contrib-python 3.4.2.17 Matplotlib 3.1.1 pyqt5 5.1

Kenny Cheng 3 Aug 17, 2022
meProp: Sparsified Back Propagation for Accelerated Deep Learning (ICML 2017)

meProp The codes were used for the paper meProp: Sparsified Back Propagation for Accelerated Deep Learning with Reduced Overfitting (ICML 2017) [pdf]

LancoPKU 107 Nov 18, 2022
Weight estimation in CT by multi atlas techniques

maweight A Python package for multi-atlas based weight estimation for CT images, including segmentation by registration, feature extraction and model

György Kovács 0 Dec 24, 2021
Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch

SRDenseNet-pytorch Implementation of paper: "Image Super-Resolution Using Dense Skip Connections" in PyTorch (http://openaccess.thecvf.com/content_ICC

wxy 114 Nov 26, 2022
A general and strong 3D object detection codebase that supports more methods, datasets and tools (debugging, recording and analysis).

ALLINONE-Det ALLINONE-Det is a general and strong 3D object detection codebase built on OpenPCDet, which supports more methods, datasets and tools (de

Michael.CV 5 Nov 03, 2022
A custom-designed Spider Robot trained to walk using Deep RL in a PyBullet Simulation

SpiderBot_DeepRL Title: Implementation of Single and Multi-Agent Deep Reinforcement Learning Algorithms for a Walking Spider Robot Authors(s): Arijit

Arijit Dasgupta 9 Jul 28, 2022
PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis

Daft-Exprt - PyTorch Implementation PyTorch Implementation of Daft-Exprt: Robust Prosody Transfer Across Speakers for Expressive Speech Synthesis The

Keon Lee 47 Dec 18, 2022
SegNet including indices pooling for Semantic Segmentation with tensorflow and keras

SegNet SegNet is a model of semantic segmentation based on Fully Comvolutional Network. This repository contains the implementation of learning and te

Yuta Kamikawa 172 Dec 23, 2022
Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021

Deep Representation One-class Classification (DROC). This is not an officially supported Google product. Tensorflow 2 implementation of the paper: Lea

Google Research 137 Dec 23, 2022
Atif Hassan 103 Dec 14, 2022
Face Recognition plus identification simply and fast | Python

PyFaceDetection Face Recognition plus identification simply and fast Ubuntu Setup sudo pip3 install numpy sudo pip3 install cmake sudo pip3 install dl

Peyman Majidi Moein 16 Sep 22, 2022
Towards Long-Form Video Understanding

Towards Long-Form Video Understanding Chao-Yuan Wu, Philipp Krähenbühl, CVPR 2021 [Paper] [Project Page] [Dataset] Citation @inproceedings{lvu2021,

Chao-Yuan Wu 69 Dec 26, 2022
Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation

Using Self-Supervised Pretext Tasks for Active Learning - Official Pytorch Implementation Experiment Setting: CIFAR10 (downloaded and saved in ./DATA

John Seon Keun Yi 38 Dec 27, 2022
The 2nd place solution of 2021 google landmark retrieval on kaggle.

Google_Landmark_Retrieval_2021_2nd_Place_Solution The 2nd place solution of 2021 google landmark retrieval on kaggle. Environment We use cuda 11.1/pyt

229 Dec 13, 2022