Pytorch implementation of Supporting Clustering with Contrastive Learning, NAACL 2021

Related tags

Deep Learningsccl
Overview

Supporting Clustering with Contrastive Learning

SCCL (NAACL 2021) Dejiao Zhang, Feng Nan, Xiaokai Wei, Shangwen Li, Henghui Zhu, Kathleen McKeown, Ramesh Nallapati, Andrew Arnold, and Bing Xiang.

Requirements

Datasets:

In additional to the original data, SCCL requires a pair of augmented data for each instance. See our paper for details.

Dependencies:

python==3.6. 
pytorch==1.6.0. 
sentence-transformers==0.3.8. 
transformers==3.3.0. 
tensorboardX==2.1.  

To run the code:

1. put your dataset in the folder "./datasamples"  # for some license issue, we are not able to release the dataset now, we'll release the datasets asap
2. bash ./scripts/run.sh # you need change the dataset info and results path accordingly

Citation:

@inproceedings{zhang-etal-2021-supporting,
title = "Supporting Clustering with Contrastive Learning",
author = "Zhang, Dejiao  and Nan, Feng  and Wei, Xiaokai  and
  Li, Shang-Wen  and Zhu, Henghui  and McKeown, Kathleen  and
  Nallapati, Ramesh  and Arnold, Andrew O.  and Xiang, Bing",
booktitle = "Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies",
month = jun,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://www.aclweb.org/anthology/2021.naacl-main.427",
pages = "5419--5430",
abstract = " ",}
ScriptProfilerPy - Module to visualize where your python script is slow

ScriptProfiler helps you track where your code is slow It provides: Code lines t

Lucas BLP 3 Jun 02, 2022
Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models

Robbing the FED: Directly Obtaining Private Data in Federated Learning with Modified Models This repo contains a barebones implementation for the atta

16 Dec 04, 2022
CNN designed for pansharpening

PROGRESSIVE BAND-SEPARATED CONVOLUTIONAL NEURAL NETWORK FOR MULTISPECTRAL PANSHARPENING This repository contains main code for the paper PROGRESSIVE B

SerendipitysX 3 Dec 29, 2021
Implements VQGAN+CLIP for image and video generation, and style transfers, based on text and image prompts. Emphasis on ease-of-use, documentation, and smooth video creation.

VQGAN-CLIP-GENERATOR Overview This is a package (with available notebook) for running VQGAN+CLIP locally, with a focus on ease of use, good documentat

Ryan Hamilton 98 Dec 30, 2022
Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning"

CAPGNN Source code and dataset of the paper "Contrastive Adaptive Propagation Graph Neural Networks forEfficient Graph Learning" Paper URL: https://ar

1 Mar 12, 2022
[ICCV 2021] Deep Hough Voting for Robust Global Registration

Deep Hough Voting for Robust Global Registration, ICCV, 2021 Project Page | Paper | Video Deep Hough Voting for Robust Global Registration Junha Lee1,

Junha Lee 10 Dec 02, 2022
[ICCV 2021] Excavating the Potential Capacity of Self-Supervised Monocular Depth Estimation

EPCDepth EPCDepth is a self-supervised monocular depth estimation model, whose supervision is coming from the other image in a stereo pair. Details ar

Rui Peng 110 Dec 23, 2022
Code for the upcoming CVPR 2021 paper

The Temporal Opportunist: Self-Supervised Multi-Frame Monocular Depth Jamie Watson, Oisin Mac Aodha, Victor Prisacariu, Gabriel J. Brostow and Michael

Niantic Labs 496 Dec 30, 2022
OpenMMLab 3D Human Parametric Model Toolbox and Benchmark

Introduction English | 简体中文 MMHuman3D is an open source PyTorch-based codebase for the use of 3D human parametric models in computer vision and comput

OpenMMLab 782 Jan 04, 2023
A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning.

Open3DSOT A general python framework for single object tracking in LiDAR point clouds, based on PyTorch Lightning. The official code release of BAT an

Kangel Zenn 172 Dec 23, 2022
a simple, efficient, and intuitive text editor

Oxygen beta a simple, efficient, and intuitive text editor Overview oxygen is a simple, efficient, and intuitive text editor designed as more featured

Aarush Gupta 1 Feb 23, 2022
Code for "Single-view robot pose and joint angle estimation via render & compare", CVPR 2021 (Oral).

Single-view robot pose and joint angle estimation via render & compare Yann Labbé, Justin Carpentier, Mathieu Aubry, Josef Sivic CVPR: Conference on C

Yann Labbé 51 Oct 14, 2022
A implemetation of the LRCN in mxnet

A implemetation of the LRCN in mxnet ##Abstract LRCN is a combination of CNN and RNN ##Installation Download UCF101 dataset ./avi2jpg.sh to split the

44 Aug 25, 2022
ToFFi - Toolbox for Frequency-based Fingerprinting of Brain Signals

ToFFi Toolbox This repository contains "before peer review" version of the software related to the preprint of the publication ToFFi - Toolbox for Fre

4 Aug 31, 2022
Simple Python application to transform Serial data into OSC messages

SerialToOSC-Bridge Simple Python application to transform Serial data into OSC messages. The current purpose is to be a compatibility layer between ha

Division of Applied Acoustics at Chalmers University of Technology 3 Jun 03, 2021
Implementation for paper LadderNet: Multi-path networks based on U-Net for medical image segmentation

Implementation for paper LadderNet: Multi-path networks based on U-Net for medical image segmentation This implementation is based on orobix implement

Juntang Zhuang 116 Sep 06, 2022
MADT: Offline Pre-trained Multi-Agent Decision Transformer

MADT: Offline Pre-trained Multi-Agent Decision Transformer A link to our paper can be found on Arxiv. Overview Official codebase for Offline Pre-train

Linghui Meng 51 Dec 21, 2022
Code for "Neural 3D Scene Reconstruction with the Manhattan-world Assumption" CVPR 2022 Oral

News 05/10/2022 To make the comparison on ScanNet easier, we provide all quantitative and qualitative results of baselines here, including COLMAP, COL

ZJU3DV 365 Dec 30, 2022
Infrastructure as Code (IaC) for a self-hosted version of Gnosis Safe on AWS

Welcome to Yearn Gnosis Safe! Setting up your local environment Infrastructure Deploying Gnosis Safe Prerequisites 1. Create infrastructure for secret

Numan 16 Jul 18, 2022
Atomistic Line Graph Neural Network

Table of Contents Introduction Installation Examples Pre-trained models Quick start using colab JARVIS-ALIGNN webapp Peformances on a few datasets Use

National Institute of Standards and Technology 91 Dec 30, 2022