SPEAR: Semi suPErvised dAta progRamming

Related tags

Deep Learningweak-supervisionsemi-superviseddata-programmingdata-efficient-learninglabeling-functions
Overview

PyPI docs license website GitHub repo size



Semi-Supervised Data Programming for Data Efficient Machine Learning

SPEAR is a library for data programming with semi-supervision. The package implements several recent data programming approaches including facility to programmatically label and build training data.

Pipeline

  • Design Labeling functions(LFs)
  • generate pickle file containing labels by passing raw data to LFs
  • Use one of the Label Aggregators(LA) to get final labels



SPEAR provides functionality such as

  • development of LFs/rules/heuristics for quick labeling
  • compare against several data programming approaches
  • compare against semi-supervised data programming approaches
  • use subset selection to make best use of the annotation efforts

Labelling Functions (LFs)

  • discrete LFs - Users can define LFs that return discrete labels
  • continuous LFs - return continuous scores/confidence to the labels assigned

Approaches Implemented

You can read this paper to know about below approaches

  • Only-L
  • Learning to Reweight
  • Posterior Regularization
  • Imply Loss
  • CAGE
  • Joint Learning

Data folder for SMS can be found here. This folder needs to be placed in the same directory as notebooks folder is in, to run the notebooks or examples.

Installation

Method 1

To install latest version of SPEAR package using PyPI:

pip install decile-spear

Method 2

SPEAR requires Python 3.6 or later. First install submodlib. Then install SPEAR:

git clone https://github.com/decile-team/spear.git
cd spear
pip install -r requirements/requirements.txt

Citation

@misc{abhishek2021spear,
      title={SPEAR : Semi-supervised Data Programming in Python}, 
      author={Guttu Sai Abhishek and Harshad Ingole and Parth Laturia and Vineeth Dorna and Ayush Maheshwari and Ganesh Ramakrishnan and Rishabh Iyer},
      year={2021},
      eprint={2108.00373},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}

Quick Links

Acknowledgment

SPEAR takes inspiration, builds upon, and uses pieces of code from several open source codebases. These include Snorkel, Snuba & Imply Loss. Also, SPEAR uses SUBMODLIB for subset selection, which is provided by DECILE too.

Team

SPEAR is created and maintained by Ayush, Abhishek, Vineeth, Harshad, Parth, Pankaj, Rishabh Iyer, and Ganesh Ramakrishnan. We look forward to have SPEAR more community driven. Please use it and contribute to it for your research, and feel free to use it for your commercial projects. We will add the major contributors here.

Publications

[1] Maheshwari, Ayush, et al. Data Programming using Semi-Supervision and Subset Selection, In Findings of ACL (Long Paper) 2021.

[2] Chatterjee, Oishik, Ganesh Ramakrishnan, and Sunita Sarawagi. Data Programming using Continuous and Quality-Guided Labeling Functions, In AAAI 2020.

[3] Sahay, Atul, et al. Rule augmented unsupervised constituency parsing, In Findings of ACL (Short Paper) 2021.

You might also like...
Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images

SASSnet Code for paper: Shape-aware Semi-supervised 3D Semantic Segmentation for Medical Images(MICCAI 2020) Our code is origin from UA-MT You can fin

klein 125 Jan 3, 2023
Semi-supervised Learning for Sentiment Analysis

Neural-Semi-supervised-Learning-for-Text-Classification-Under-Large-Scale-Pretraining Code, models and Datasets for《Neural Semi-supervised Learning fo

null 47 Jan 1, 2023
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

Jia Research Lab 137 Dec 14, 2022
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (CVPR'2020)
From Fidelity to Perceptual Quality: A Semi-Supervised Approach for Low-Light Image Enhancement (CVPR'2020)

Under-exposure introduces a series of visual degradation, i.e. decreased visibility, intensive noise, and biased color, etc. To address these problems, we propose a novel semi-supervised learning approach for low-light image enhancement.

Yang Wenhan 117 Jan 3, 2023
Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021, Pytorch)

S2VD Semi-supervised Video Deraining with Dynamical Rain Generator (CVPR, 2021) Requirements and Dependencies Ubuntu 16.04, cuda 10.0 Python 3.6.10, P

Zongsheng Yue 53 Nov 23, 2022
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.
[CVPR 2021] MiVOS - Mask Propagation module. Reproduced STM (and better) with training code :star2:. Semi-supervised video object segmentation evaluation.

MiVOS (CVPR 2021) - Mask Propagation Ho Kei Cheng, Yu-Wing Tai, Chi-Keung Tang [arXiv] [Paper PDF] [Project Page] [Papers with Code] This repo impleme

Rex Cheng 106 Jan 3, 2023
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)
Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation (CVPR 2021)

Anti-Adversarially Manipulated Attributions for Weakly and Semi-Supervised Semantic Segmentation Input Image Initial CAM Successive Maps with adversar

Jungbeom Lee 110 Dec 7, 2022
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)
Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CVPR 2021)

Semi-supervised Semantic Segmentation with Directional Context-aware Consistency (CAC) Xin Lai*, Zhuotao Tian*, Li Jiang, Shu Liu, Hengshuang Zhao, Li

DV Lab 137 Dec 14, 2022
[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
[CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision

TorchSemiSeg [CVPR 2021] Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision by Xiaokang Chen1, Yuhui Yuan2, Gang Zeng1, Jingdong Wang

Chen XiaoKang 387 Jan 8, 2023
Comments
  • Updated condition for Gold Label check and passing parameter name passing

    Updated condition for Gold Label check and passing parameter name passing

    1. Current Version of Spear fails when we are trying to do LF analysis without passing Gold Labels and their values is passed as None and is causing the following error as it is not checked

    Y = np.array([self.mapping[v] for v in Y]) TypeError: 'NoneType' object is not iterable

    1. Also their is a function call of confusion_matrix in lf_summary method, which requires the parameter name to execute properly else it fails with following error of argument passing

    confusion_matrix(Y, self.L[:, i], labels)[1:, 1:] for i in range(m) TypeError: confusion_matrix() takes 2 positional arguments but 3 were given

    The current code change fixes these two issues.

    opened by kasuba-badri-vishal 1
  • sms_jl.ipynb ISSUE with

    sms_jl.ipynb ISSUE with "Some Labelling Functions" code snippet

    I have changed the directory of previously glove_w2v.txt and then ran on my local pc and installed all reqd libraries but it shows an invalid literal for int() with base 10: 'import'

    I think its an issue with gensim but can;t seem to resolve it

    i'm attaching a picture down below :

    https://cdn.discordapp.com/attachments/754057588714373325/989172192078098442/unknown.png

    opened by Brshank 1
Releases(v1.0.0)
Owner
decile-team
DECILE: Data EffiCient machIne LEarning
decile-team
GitHub Repository https://spear-decile.readthedocs.io/
An executor that performs image segmentation on fashion items

ClothingSegmenter U2NET fashion image/clothing segmenter based on https://github.com/levindabhi/cloth-segmentation Overview The ClothingSegmenter exec

Jina AI 5 Mar 30, 2022
Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021)

TDEER (WIP) Code For TDEER: An Efficient Translating Decoding Schema for Joint Extraction of Entities and Relations (EMNLP2021) Overview TDEER is an e

Alipay 6 Dec 17, 2022
CNN Based Meta-Learning for Noisy Image Classification and Template Matching

CNN Based Meta-Learning for Noisy Image Classification and Template Matching Introduction This master thesis used a few-shot meta learning approach to

Kumar Manas 2 Dec 09, 2021
Builds a LoRa radio frequency fingerprint identification (RFFI) system based on deep learning techiniques

This project builds a LoRa radio frequency fingerprint identification (RFFI) system based on deep learning techiniques.

20 Dec 30, 2022
Fast and robust certifiable relative pose estimation

Fast and Robust Relative Pose Estimation for Calibrated Cameras This repository contains the code for the relative pose estimation between two central

42 Dec 06, 2022
Patch SVDD for Image anomaly detection

Patch SVDD Patch SVDD for Image anomaly detection. Paper: https://arxiv.org/abs/2006.16067 (published in ACCV 2020). Original Code : https://github.co

Hong-Jeongmin 0 Dec 03, 2021
StarGAN - Official PyTorch Implementation (CVPR 2018)

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

Yunjey Choi 5.1k Dec 30, 2022
[ICLR 2022] DAB-DETR: Dynamic Anchor Boxes are Better Queries for DETR

DAB-DETR This is the official pytorch implementation of our ICLR 2022 paper DAB-DETR. Authors: Shilong Liu, Feng Li, Hao Zhang, Xiao Yang, Xianbiao Qi

336 Dec 25, 2022
Repo público onde postarei meus estudos de Python, buscando aprender por meio do compartilhamento do aprendizado!

Seja bem vindo à minha repo de Estudos em Python 3! Este é um repositório criado por um programador amador que estuda tópicos de finanças, estatística

32 Dec 24, 2022
Plover-tapey-tape: an alternative to Plover’s built-in paper tape

plover-tapey-tape plover-tapey-tape is an alternative to Plover’s built-in paper

7 May 29, 2022
PyTorch implementation of the paper The Lottery Ticket Hypothesis for Object Recognition

LTH-ObjectRecognition The Lottery Ticket Hypothesis for Object Recognition Sharath Girish*, Shishira R Maiya*, Kamal Gupta, Hao Chen, Larry Davis, Abh

16 Feb 06, 2022
Implement the Pareto Optimizer and pcgrad to make a self-adaptive loss for multi-task

multi-task_losses_optimizer Implement the Pareto Optimizer and pcgrad to make a self-adaptive loss for multi-task 已经实验过了，不会有cuda out of memory情况 ##Par

14 Dec 25, 2022
An exploration of log domain "alternative floating point" for hardware ML/AI accelerators.

This repository contains the SystemVerilog RTL, C++, HLS (Intel FPGA OpenCL to wrap RTL code) and Python needed to reproduce the numerical results in

Facebook Research 373 Dec 31, 2022
High-Fidelity Pluralistic Image Completion with Transformers (ICCV 2021)

Image Completion Transformer (ICT) Project Page | Paper (ArXiv) | Pre-trained Models | Supplemental Material This repository is the official pytorch i

Ziyu Wan 243 Jan 03, 2023
An offline deep reinforcement learning library

d3rlpy: An offline deep reinforcement learning library d3rlpy is an offline deep reinforcement learning library for practitioners and researchers. imp

Takuma Seno 817 Jan 02, 2023
PyTorch framework for Deep Learning research and development.

Accelerated DL & RL PyTorch framework for Deep Learning research and development. It was developed with a focus on reproducibility, fast experimentati

Catalyst-Team 29 Jul 13, 2022
IOT: Instance-wise Layer Reordering for Transformer Structures

Introduction This repository contains the code for Instance-wise Ordered Transformer (IOT), which is introduced in the ICLR2021 paper IOT: Instance-wi

IOT 19 Nov 15, 2022
Official PyTorch code of Holistic 3D Scene Understanding from a Single Image with Implicit Representation (CVPR 2021)

Implicit3DUnderstanding (Im3D) [Project Page] Holistic 3D Scene Understanding from a Single Image with Implicit Representation Cheng Zhang, Zhaopeng C

Cheng Zhang 149 Jan 08, 2023
A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model

This repository contains the similarity metrics designed and evaluated in the paper, and instructions and code to re-run the experiments. Implementation in the deep-learning framework PyTorch

Steffen 86 Dec 27, 2022
A PyTorch implementation of the paper Mixup: Beyond Empirical Risk Minimization in PyTorch

Mixup: Beyond Empirical Risk Minimization in PyTorch This is an unofficial PyTorch implementation of mixup: Beyond Empirical Risk Minimization. The co

Harry Yang 121 Dec 17, 2022