ECLARE: Extreme Classification with Label Graph Correlations

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
DARTS-: Robustly Stepping out of Performance Collapse Without Indicators

[ICLR'21] DARTS-: Robustly Stepping out of Performance Collapse Without Indicators [openreview] Authors: Xiangxiang Chu, Xiaoxing Wang, Bo Zhang, Shun

55 Nov 01, 2022
Reliable probability face embeddings

ProbFace, arxiv This is a demo code of training and testing [ProbFace] using Tensorflow. ProbFace is a reliable Probabilistic Face Embeddging (PFE) me

Kaen Chan 34 Dec 31, 2022
3D-aware GANs based on NeRF (arXiv).

CIPS-3D This repository will contain the code of the paper, CIPS-3D: A 3D-Aware Generator of GANs Based on Conditionally-Independent Pixel Synthesis.

Peterou 563 Dec 31, 2022
The official repository for "Revealing unforeseen diagnostic image features with deep learning by detecting cardiovascular diseases from apical four-chamber ultrasounds"

Revealing unforeseen diagnostic image features with deep learning by detecting cardiovascular diseases from apical four-chamber ultrasounds The why Im

3 Mar 29, 2022
A simple baseline for the 2022 IEEE GRSS Data Fusion Contest (DFC2022)

DFC2022 Baseline A simple baseline for the 2022 IEEE GRSS Data Fusion Contest (DFC2022) This repository uses TorchGeo, PyTorch Lightning, and Segmenta

isaac 24 Nov 28, 2022
Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras (ICCV 2021)

N-ImageNet: Towards Robust, Fine-Grained Object Recognition with Event Cameras Official PyTorch implementation of N-ImageNet: Towards Robust, Fine-Gra

32 Dec 26, 2022
Pytorch implementation of "ARM: Any-Time Super-Resolution Method"

ARM-Net Dependencies Python 3.6 Pytorch 1.7 Results Train Data preprocessing cd data_scripts python extract_subimages_test.py python data_augmentation

Bohong Chen 55 Nov 24, 2022
Code for "Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis

HAABSAStar Code for "Adversarial Training for a Hybrid Approach to Aspect-Based Sentiment Analysis". This project builds on the code from https://gith

1 Sep 14, 2020
Image Super-Resolution by Neural Texture Transfer

SRNTT: Image Super-Resolution by Neural Texture Transfer Tensorflow implementation of the paper Image Super-Resolution by Neural Texture Transfer acce

Zhifei Zhang 413 Nov 30, 2022
Code accompanying "Adaptive Methods for Aggregated Domain Generalization"

Adaptive Methods for Aggregated Domain Generalization (AdaClust) Official Pytorch Implementation of Adaptive Methods for Aggregated Domain Generalizat

Xavier Thomas 15 Sep 20, 2022
Revisting Open World Object Detection

Revisting Open World Object Detection Installation See INSTALL.md. Dataset Our new data division is based on COCO2017. We divide the training set into

58 Dec 23, 2022
MetaDrive: Composing Diverse Scenarios for Generalizable Reinforcement Learning

MetaDrive: Composing Diverse Driving Scenarios for Generalizable RL [ Documentation | Demo Video ] MetaDrive is a driving simulator with the following

DeciForce: Crossroads of Machine Perception and Autonomy 276 Jan 04, 2023
Aggragrating Nested Transformer Official Jax Implementation

NesT is a simple method, which aggragrates nested local transformers on image blocks. The idea makes vision transformers attain better accuracy, data efficiency, and convergence on the ImageNet bench

Google Research 169 Dec 20, 2022
Course materials for Fall 2021 "CIS6930 Topics in Computing for Data Science" at New College of Florida

Fall 2021 CIS6930 Topics in Computing for Data Science This repository hosts course materials used for a 13-week course "CIS6930 Topics in Computing f

Yoshi Suhara 101 Nov 30, 2022
Digan - Official PyTorch implementation of Generating Videos with Dynamics-aware Implicit Generative Adversarial Networks

DIGAN (ICLR 2022) Official PyTorch implementation of "Generating Videos with Dyn

Sihyun Yu 147 Dec 31, 2022
Deep Learning and Reinforcement Learning Library for Scientists and Engineers 🔥

TensorLayer is a novel TensorFlow-based deep learning and reinforcement learning library designed for researchers and engineers. It provides an extens

TensorLayer Community 7.1k Dec 29, 2022
A denoising autoencoder + adversarial losses and attention mechanisms for face swapping.

faceswap-GAN Adding Adversarial loss and perceptual loss (VGGface) to deepfakes'(reddit user) auto-encoder architecture. Updates Date Update 2018-08-2

3.2k Dec 30, 2022
Multilingual Image Captioning

Multilingual Image Captioning Authors: Bhavitvya Malik, Gunjan Chhablani Demo Link: https://huggingface.co/spaces/flax-community/multilingual-image-ca

Gunjan Chhablani 32 Nov 25, 2022
Credit fraud detection in Python using a Jupyter Notebook

Credit-Fraud-Detection - Credit fraud detection in Python using a Jupyter Notebook , using three classification models (Random Forest, Gaussian Naive Bayes, Logistic Regression) from the sklearn libr

Ali Akram 4 Dec 28, 2021
Code for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelines with Query Variation Generators"

Query Variation Generators This repository contains the code and annotation data for the ECIR'22 paper "Evaluating the Robustness of Retrieval Pipelin

Gustavo Penha 12 Nov 20, 2022