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]

Programming with Neural Surrogates of Programs

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight).

ARKitScenes - A Diverse Real-World Dataset for 3D Indoor Scene Understanding Using Mobile RGB-D Data

R-Drop: Regularized Dropout for Neural Networks

On Effective Scheduling of Model-based Reinforcement Learning

A certifiable defense against adversarial examples by training neural networks to be provably robust

Semi-supervised semantic segmentation needs strong, varied perturbations

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

DP-CL(Continual Learning with Differential Privacy)

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

Toolkit for collecting and applying prompts

Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 short.

"SOLQ: Segmenting Objects by Learning Queries", SOLQ is an end-to-end instance segmentation framework with Transformer.

Efficient and intelligent interactive segmentation annotation software

Intrusion Detection System using ensemble learning (machine learning)

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

(under submission) Bayesian Integration of a Generative Prior for Image Restoration

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

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]

Programming with Neural Surrogates of Programs

Pytorch Implementations of large number classical backbone CNNs, data enhancement, torch loss, attention, visualization and some common algorithms.

The official PyTorch implementation of Curriculum by Smoothing (NeurIPS 2020, Spotlight).

ARKitScenes - A Diverse Real-World Dataset for 3D Indoor Scene Understanding Using Mobile RGB-D Data

R-Drop: Regularized Dropout for Neural Networks

On Effective Scheduling of Model-based Reinforcement Learning

A certifiable defense against adversarial examples by training neural networks to be provably robust

Semi-supervised semantic segmentation needs strong, varied perturbations

HistoSeg : Quick attention with multi-loss function for multi-structure segmentation in digital histology images

DP-CL(Continual Learning with Differential Privacy)

Pynomial - a lightweight python library for implementing the many confidence intervals for the risk parameter of a binomial model

Useful materials and tutorials for 110-1 NTU DBME5028 (Application of Deep Learning in Medical Imaging)

Toolkit for collecting and applying prompts

Official repository for "Exploiting Session Information in BERT-based Session-aware Sequential Recommendation", SIGIR 2022 short.

"SOLQ: Segmenting Objects by Learning Queries", SOLQ is an end-to-end instance segmentation framework with Transformer.

Efficient and intelligent interactive segmentation annotation software

Intrusion Detection System using ensemble learning (machine learning)

Just playing with getting VQGAN+CLIP running locally, rather than having to use colab.

(under submission) Bayesian Integration of a Generative Prior for Image Restoration

Code to run experiments in SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression.

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)