ECLARE: Extreme Classification with Label Graph Correlations

Related tags

Deep Learningpythonmachine-learningdeeplearningmulti-label-classificationgcnmeta-dataextreme-classificationlabel-textgraph-correlationlabel-correlationseclare
Overview

ECLARE

ECLARE: Extreme Classification with Label Graph Correlations

@InProceedings{Mittal21b,
	author       = "Mittal, A. and Sachdeva, N. and Agrawal, S. and Agarwal, S. and Kar, P. and Varma, M.",
	title        = "ECLARE: Extreme classification with label graph correlations",
	booktitle    = "Proceedings of The ACM International World Wide Web Conference",
	month = "April",
	year = "2021",
	}

SETUP WORKSPACE

mkdir -p ${HOME}/scratch/XC/data 
mkdir -p ${HOME}/scratch/XC/programs

SETUP ECLARE

cd ${HOME}/scratch/XC/programs
git clone https://github.com/Extreme-classification/ECLARE.git
conda create -f ECLARE/eclare_env.yml
conda activate eclare
git clone https://github.com/kunaldahiya/pyxclib.git
cd pyxclib
python setup.py install
cd ../ECLARE

DOWNLOAD DATASET

cd ${HOME}/scratch/XC/data
gdown --id <dataset id>
unzip *.zip
dataset dataset id
LF-AmazonTitles-131K 1VlfcdJKJA99223fLEawRmrXhXpwjwJKn
LF-WikiSeeAlsoTitles-131K 1edWtizAFBbUzxo9Z2wipGSEA9bfy5mdX
LF-AmazonTitles-1.3M 1Davc6BIfoTIAS3mP1mUY5EGcGr2zN2pO

RUNNING ECLARE

cd ${HOME}/scratch/XC/programs/ECLARE
chmod +x run_ECLARE.sh
./run_ECLARE.sh <gpu_id> <ECLARE TYPE> <dataset> <folder name>
e.g.
./run_ECLARE.sh 0 ECLARE LF-AmazonTitles-131K ECLARE_RUN

YOU MAY ALSO LIKE

Owner
Extreme Classification
Extreme Classification
GitHub Repository
Differentiable molecular simulation of proteins with a coarse-grained potential

Differentiable molecular simulation of proteins with a coarse-grained potential This repository contains the learned potential, simulation scripts and

UCL Bioinformatics Group 44 Dec 10, 2022
Code for the paper "Curriculum Dropout", ICCV 2017

Curriculum Dropout Dropout is a very effective way of regularizing neural networks. Stochastically "dropping out" units with a certain probability dis

Pietro Morerio 21 Jan 02, 2022
Self-Supervised CNN-GCN Autoencoder

GCNDepth Self-Supervised CNN-GCN Autoencoder GCNDepth: Self-supervised monocular depth estimation based on graph convolutional network To be published

53 Dec 14, 2022
Deep Multi-Magnification Network for multi-class tissue segmentation of whole slide images

Deep Multi-Magnification Network This repository provides training and inference codes for Deep Multi-Magnification Network published here. Deep Multi

Computational Pathology 12 Aug 06, 2022
A smaller subset of 10 easily classified classes from Imagenet, and a little more French

Imagenette 🎶 Imagenette, gentille imagenette, Imagenette, je te plumerai. 🎶 (Imagenette theme song thanks to Samuel Finlayson) NB: Versions of Image

fast.ai 718 Jan 01, 2023
TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A good teacher is patient and consistent by Beyer et al.

FunMatch-Distillation TF2 implementation of knowledge distillation using the "function matching" hypothesis from the paper Knowledge distillation: A g

Sayak Paul 67 Dec 20, 2022
CLADE - Efficient Semantic Image Synthesis via Class-Adaptive Normalization (TPAMI 2021)

Efficient Semantic Image Synthesis via Class-Adaptive Normalization (Accepted by TPAMI)

tzt 49 Nov 17, 2022
Learning Efficient Online 3D Bin Packing on Packing Configuration Trees

Learning Efficient Online 3D Bin Packing on Packing Configuration Trees This repository is being continuously updated, please stay tuned！ Any code con

86 Dec 28, 2022
Anchor-free Oriented Proposal Generator for Object Detection

Anchor-free Oriented Proposal Generator for Object Detection Gong Cheng, Jiabao Wang, Ke Li, Xingxing Xie, Chunbo Lang, Yanqing Yao, Junwei Han, Intro

jbwang1997 56 Nov 15, 2022
Paper Code：A Self-adaptive Weighted Differential Evolution Approach for Large-scale Feature Selection

1. SaWDE.m is the main function 2. DataPartition.m is used to randomly partition the original data into training sets and test sets with a ratio of 7

wangxb 14 Dec 08, 2022
ICRA 2021 - Robust Place Recognition using an Imaging Lidar

Robust Place Recognition using an Imaging Lidar A place recognition package using high-resolution imaging lidar. For best performance, a lidar equippe

Tixiao Shan 293 Dec 27, 2022
Towards Fine-Grained Reasoning for Fake News Detection

FinerFact This is the PyTorch implementation for the FinerFact model in the AAAI 2022 paper Towards Fine-Grained Reasoning for Fake News Detection (Ar

Ahren_Jin 15 Dec 15, 2022
Easy to use Audio Tagging in PyTorch

Audio Classification, Tagging & Sound Event Detection in PyTorch Progress: Fine-tune on audio classification Fine-tune on audio tagging Fine-tune on s

sithu3 15 Dec 22, 2022
Unity Propagation in Bayesian Networks Handling Inconsistency via Unity Smoothing

This repository contains the scripts needed to generate the results from the paper Unity Propagation in Bayesian Networks Handling Inconsistency via U

0 Jan 19, 2022
Contrastive Learning Inverts the Data Generating Process

Official code to reproduce the results and data presented in the paper Contrastive Learning Inverts the Data Generating Process.

71 Nov 25, 2022
Code for the paper: Adversarial Machine Learning: Bayesian Perspectives

Code for the paper: Adversarial Machine Learning: Bayesian Perspectives This repository contains code for reproducing the experiments in the ** Advers

Roi Naveiro 2 Nov 11, 2022
Cross View SLAM

Cross View SLAM This is the associated code and dataset repository for our paper I. D. Miller et al., "Any Way You Look at It: Semantic Crossview Loca

Ian D. Miller 99 Dec 09, 2022
imbalanced-DL: Deep Imbalanced Learning in Python

imbalanced-DL: Deep Imbalanced Learning in Python Overview imbalanced-DL (imported as imbalanceddl) is a Python package designed to make deep imbalanc

NTUCSIE CLLab 19 Dec 28, 2022
Using Tensorflow Object Detection API to detect Waymo open dataset

Waymo-2D-Object-Detection Using Tensorflow Object Detection API to detect Waymo open dataset Result CenterNet Training Loss SSD ResNet Training Loss C

76 Dec 12, 2022
[ICCV'21] NEAT: Neural Attention Fields for End-to-End Autonomous Driving

NEAT: Neural Attention Fields for End-to-End Autonomous Driving Paper | Supplementary | Video | Poster | Blog This repository is for the ICCV 2021 pap

254 Jan 02, 2023