Automated Evidence Collection for Fake News Detection

Last update: Apr 12, 2022

Related tags

Overview

Automated Evidence Collection for Fake News Detection

This is the code repo for the Automated Evidence Collection for Fake News Detection paper accepted to be published at the eighteenth International Conference on Natural Language Processing (ICON), 2021.

The paper description, PDF, slides and video are available here:

http://dipteshkanojia.github.io/publication/icon-2021-fakenews-evidence/

For the CONSTRAINT Dataset used in the paper, please refer to:

https://constraint-shared-task-2021.github.io/

If you use this study in your research, please cite:

@misc{rawat2021automated,
      title={Automated Evidence Collection for Fake News Detection}, 
      author={Mrinal Rawat and Diptesh Kanojia},
      year={2021},
      eprint={2112.06507},
      archivePrefix={arXiv},
      primaryClass={cs.CL}
}

Owner

Mrinal Rawat

Researcher | NLP | ML | DL

GitHub Repository

Cervix ROI Segmentation Using U-NET

Cervix ROI Segmentation Using U-NET Overview This code illustrate how to segment the ROI in cervical images using U-NET. The ROI here meant to include

35 Sep 14, 2022

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes Introduction This is the unofficial code of Deep Dual-re

113 Dec 23, 2022

PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-supervised ViT.

MAE for Self-supervised ViT Introduction This is an unofficial PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-sup

36 Oct 30, 2022

Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Representation Robustness Evaluations Our implementation is based on code from MadryLab's robustness package and Devon Hjelm's Deep InfoMax. For all t

19 Dec 07, 2022

Automated Evidence Collection for Fake News Detection

Related tags

Overview

Automated Evidence Collection for Fake News Detection

The paper description, PDF, slides and video are available here:

For the CONSTRAINT Dataset used in the paper, please refer to:

If you use this study in your research, please cite:

Owner

Mrinal Rawat

Cervix ROI Segmentation Using U-NET

This is the unofficial code of Deep Dual-resolution Networks for Real-time and Accurate Semantic Segmentation of Road Scenes. which achieve state-of-the-art trade-off between accuracy and speed on cityscapes and camvid, without using inference acceleration and extra data

PyTorch implementation of Masked Autoencoders Are Scalable Vision Learners for self-supervised ViT.

Code for the paper: Learning Adversarially Robust Representations via Worst-Case Mutual Information Maximization (https://arxiv.org/abs/2002.11798)

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Multiband spectro-radiometric satellite image analysis with K-means cluster algorithm

Learning Continuous Signed Distance Functions for Shape Representation

Code implementation for the paper 'Conditional Gaussian PAC-Bayes'.

Official repository of my book: "Deep Learning with PyTorch Step-by-Step: A Beginner's Guide"

This repository lets you interact with Lean through a REPL.

Efficient-GlobalPointer - Pytorch Efficient GlobalPointer

Tensorflow implementation of "Learning Deep Features for Discriminative Localization"

DeLighT: Very Deep and Light-Weight Transformers

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

Transformer in Vision

Prososdy Morph: A python library for manipulating pitch and duration in an algorithmic way, for resynthesizing speech.

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

Official Matlab Implementation for "Tiny Obstacle Discovery by Occlusion-aware Multilayer Regression", TIP 2020

Uncertainty Estimation via Response Scaling for Pseudo-mask Noise Mitigation in Weakly-supervised Semantic Segmentation

Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021, Pytorch)