Asterisk is a framework to generate high-quality training datasets at scale

Related tags

Deep LearningAsterisk
Overview

Asterisk*

Generating Training Data made Easy

Asterisk is a framework to generate high-quality training datasets at scale. Instead of relying on the end users to write user-defined heuristics, the proposed approach exploits a small set of labeled data and automatically produces a set of heuristics to assign initial labels. In order to enhance the quality of the generated labels, the framework improves the accuracies of the heuristics by applying a novel data-driven AL process. During the process, the system examines the generated weak labels along with the modeled accuracies of the heuristics to help the learner decide on the points for which the user should provide true labels.

Installation

To install Asterisk, you can use pip:

pip install asterisk

or clone the Git repository and run:

pip install -e .

within it.

Publications

  • M. Nashaat, A. Ghosh, J. Miller, and S. Quader, “Asterisk: Generating Large Training Datasets with Automatic Active Supervision,” ACM Transactions on Data Science (TDS), May 2020.
  • M. Nashaat, A. Ghosh, J. Miller, and S. Quader, "WeSAL: Applying Active Supervision to Find High-quality Labels at Industrial Scale", Proceedings of the 53rd Hawaii International Conference on System Sciences, HI, USA, 2020, pp. 219-228.
  • M. Nashaat, A. Ghosh, J. Miller, S. Quader, C. Marston and J. Puget, "Hybridization of Active Learning and Data Programming for Labeling Large Industrial Datasets," 2018 IEEE International Conference on Big Data (Big Data) , Seattle, WA, USA, 2018, pp. 46-55. doi: 10.1109/BigData.2018.8622459.
Owner
Mona Nashaat
Mona Nashaat
To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

To propose and implement a multi-class classification approach to disaster assessment from the given data set of post-earthquake satellite imagery.

Kunal Wadhwa 2 Jan 05, 2022
Independent and minimal implementations of some reinforcement learning algorithms using PyTorch (including PPO, A3C, A2C, ...).

PyTorch RL Minimal Implementations There are implementations of some reinforcement learning algorithms, whose characteristics are as follow: Less pack

Gemini Light 4 Dec 31, 2022
Official Pytorch Implementation of: "ImageNet-21K Pretraining for the Masses"(2021) paper

ImageNet-21K Pretraining for the Masses Paper | Pretrained models Official PyTorch Implementation Tal Ridnik, Emanuel Ben-Baruch, Asaf Noy, Lihi Zelni

574 Jan 02, 2023
From Canonical Correlation Analysis to Self-supervised Graph Neural Networks

Code for CCA-SSG model proposed in the NeurIPS 2021 paper From Canonical Correlation Analysis to Self-supervised Graph Neural Networks.

Hengrui Zhang 44 Nov 27, 2022
Grammar Induction using a Template Tree Approach

Gitta Gitta ("Grammar Induction using a Template Tree Approach") is a method for inducing context-free grammars. It performs particularly well on data

Thomas Winters 36 Nov 15, 2022
PyTorch implementation of EfficientNetV2

[NEW!] Check out our latest work involution accepted to CVPR'21 that introduces a new neural operator, other than convolution and self-attention. PyTo

Duo Li 375 Jan 03, 2023
A PyTorch-based Semi-Supervised Learning (SSL) Codebase for Pixel-wise (Pixel) Vision Tasks

PixelSSL is a PyTorch-based semi-supervised learning (SSL) codebase for pixel-wise (Pixel) vision tasks. The purpose of this project is to promote the

Zhanghan Ke 255 Dec 11, 2022
[CVPR 2022 Oral] TubeDETR: Spatio-Temporal Video Grounding with Transformers

TubeDETR: Spatio-Temporal Video Grounding with Transformers Website • STVG Demo • Paper This repository provides the code for our paper. This includes

Antoine Yang 108 Dec 27, 2022
Source code of D-HAN: Dynamic News Recommendation with Hierarchical Attention Network

D-HAN The source code of D-HAN This is the source code of D-HAN: Dynamic News Recommendation with Hierarchical Attention Network. However, only the co

30 Sep 22, 2022
Deep generative models of 3D grids for structure-based drug discovery

What is liGAN? liGAN is a research codebase for training and evaluating deep generative models for de novo drug design based on 3D atomic density grid

Matt Ragoza 152 Jan 03, 2023
Recognize Handwritten Digits using Deep Learning on the browser itself.

MNIST on the Web An attempt to predict MNIST handwritten digits from my PyTorch model from the browser (client-side) and not from the server, with the

Harjyot Bagga 7 May 28, 2022
RATCHET is a Medical Transformer for Chest X-ray Diagnosis and Reporting

RATCHET: RAdiological Text Captioning for Human Examined Thoraxes RATCHET is a Medical Transformer for Chest X-ray Diagnosis and Reporting. Based on t

26 Nov 14, 2022
Official Implementation of VAT

Semantic correspondence Few-shot segmentation Cost Aggregation Is All You Need for Few-Shot Segmentation For more information, check out project [Proj

Hamacojr 114 Dec 27, 2022
Algorithm to texture 3D reconstructions from multi-view stereo images

MVS-Texturing Welcome to our project that textures 3D reconstructions from images. This project focuses on 3D reconstructions generated using structur

Nils Moehrle 766 Jan 04, 2023
A toolkit for controlling Euro Truck Simulator 2 with python to develop self-driving algorithms.

europilot Overview Europilot is an open source project that leverages the popular Euro Truck Simulator(ETS2) to develop self-driving algorithms. A con

1.4k Jan 04, 2023
Patch-Based Deep Autoencoder for Point Cloud Geometry Compression

Patch-Based Deep Autoencoder for Point Cloud Geometry Compression Overview The ever-increasing 3D application makes the point cloud compression unprec

17 Dec 05, 2022
ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis

ImageBART NeurIPS 2021 Patrick Esser*, Robin Rombach*, Andreas Blattmann*, Björn Ommer * equal contribution arXiv | BibTeX | Poster Requirements A sui

CompVis Heidelberg 110 Jan 01, 2023
Generalized Random Forests

generalized random forests A pluggable package for forest-based statistical estimation and inference. GRF currently provides non-parametric methods fo

GRF Labs 781 Dec 25, 2022
Elastic weight consolidation technique for incremental learning.

Overcoming-Catastrophic-forgetting-in-Neural-Networks Elastic weight consolidation technique for incremental learning. About Use this API if you dont

Shivam Saboo 89 Dec 22, 2022
Official Pytorch Implementation of Relational Self-Attention: What's Missing in Attention for Video Understanding

Relational Self-Attention: What's Missing in Attention for Video Understanding This repository is the official implementation of "Relational Self-Atte

mandos 43 Dec 07, 2022