The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

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Last update: Oct 14, 2022

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Overview

The Rich Get Richer:
Disparate Impact of Semi-Supervised Learning

Preprocess file of the dataset used in implicit sub-populations:
(Demographic groups: race and gender)

The following code will pre-process the jigsaw dataset and return train/test dataset files including demographic groups information.

Step-1:

Download the jigsaw dataset: identity_individual_annotations.csv from

https://www.kaggle.com/c/jigsaw-unintended-bias-in-toxicity-classification/data.

Step-2:

python preprocecss_jiasaw_toxicity_gender_and_race_balanced.py

Implementation of SSL methods

Please follow the official implementations of MixMatch, MixText, and UDA.

[1] https://github.com/google-research/mixmatch

[2] https://github.com/GT-SALT/MixText

[3] https://github.com/google-research/uda

Owner

[email protected]

REsponsible & Accountable Learning (REAL) @ University of California, Santa Cruz

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The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

Related tags

Overview

The Rich Get Richer:
Disparate Impact of Semi-Supervised Learning

Preprocess file of the dataset used in implicit sub-populations:
(Demographic groups: race and gender)

Step-1:

Step-2:

Implementation of SSL methods

Owner

[email protected]

pytorch implementation of "Contrastive Multiview Coding", "Momentum Contrast for Unsupervised Visual Representation Learning", and "Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination"

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AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

hipCaffe: the HIP port of Caffe

(AAAI2020)Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

[ECCV2020] Content-Consistent Matching for Domain Adaptive Semantic Segmentation

This repo contains the source code and a benchmark for predicting user's utilities with Machine Learning techniques for Computational Persuasion

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Parametric Contrastive Learning (ICCV2021)

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

Related tags

Overview

The Rich Get Richer: Disparate Impact of Semi-Supervised Learning

Preprocess file of the dataset used in implicit sub-populations: (Demographic groups: race and gender)

Step-1:

Step-2:

Implementation of SSL methods

Owner

[email protected]

pytorch implementation of "Contrastive Multiview Coding", "Momentum Contrast for Unsupervised Visual Representation Learning", and "Unsupervised Feature Learning via Non-Parametric Instance-level Discrimination"

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren*, Raymond A. Yeh*, Alexander G. Schwing.

Python PID Tuner - Makes a model of the System from a Process Reaction Curve and calculates PID Gains

Real-Time Multi-Contact Model Predictive Control via ADMM

Face Recognition plus identification simply and fast | Python

https://arxiv.org/abs/2102.11005

Simple PyTorch hierarchical models.

AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video Recognition

hipCaffe: the HIP port of Caffe

(AAAI2020)Grapy-ML: Graph Pyramid Mutual Learning for Cross-dataset Human Parsing

[ECCV2020] Content-Consistent Matching for Domain Adaptive Semantic Segmentation

This repo contains the source code and a benchmark for predicting user's utilities with Machine Learning techniques for Computational Persuasion

A Broad Study on the Transferability of Visual Representations with Contrastive Learning

aka "Bayesian Methods for Hackers": An introduction to Bayesian methods + probabilistic programming with a computation/understanding-first, mathematics-second point of view. All in pure Python ;)

Code for the paper BERT might be Overkill: A Tiny but Effective Biomedical Entity Linker based on Residual Convolutional Neural Networks

PyTorch implementation of an end-to-end Handwritten Text Recognition (HTR) system based on attention encoder-decoder networks

An implementation of the 1. Parallel, 2. Streaming, 3. Randomized SVD using MPI4Py

Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks

Multi-Output Gaussian Process Toolkit

Parametric Contrastive Learning (ICCV2021)

The Rich Get Richer:
Disparate Impact of Semi-Supervised Learning

Preprocess file of the dataset used in implicit sub-populations:
(Demographic groups: race and gender)

code for paper "Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning" by Zhongzheng Ren, Raymond A. Yeh, Alexander G. Schwing.