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]

Various operations like path tracking, counting, etc by using yolov5

This is a official repository of SimViT.

A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Data reduction pipeline for KOALA on the AAT.

The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal Transport Maps, ICLR 2022.

[ArXiv 2021] One-Shot Generative Domain Adaptation

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

FasterAI: A library to make smaller and faster models with FastAI.

Train the HRNet model on ImageNet

OBBDetection is a oriented object detection library, which is based on MMdetection.

Classification Modeling: Probability of Default

Approaches to modeling terrain and maps in python

Experiments for Fake News explainability project

Deep Reinforcement Learning for Keras.

Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.

PyTorch implementations of the NeRF model described in "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"

PyTorch ,ONNX and TensorRT implementation of YOLOv4

Adaptive FNO transformer - official Pytorch implementation

Code for the Shortformer model, from the paper by Ofir Press, Noah A. Smith and Mike Lewis.

GAT - Graph Attention Network (PyTorch) 💻 + graphs + 📣 = ❤️

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]

Various operations like path tracking, counting, etc by using yolov5

This is a official repository of SimViT.

A repository for the paper "Improved Adversarial Systems for 3D Object Generation and Reconstruction".

Data reduction pipeline for KOALA on the AAT.

The repository contains reproducible PyTorch source code of our paper Generative Modeling with Optimal Transport Maps, ICLR 2022.

[ArXiv 2021] One-Shot Generative Domain Adaptation

Bridging the Gap between Label- and Reference based Synthesis(ICCV 2021)

FasterAI: A library to make smaller and faster models with FastAI.

Train the HRNet model on ImageNet

OBBDetection is a oriented object detection library, which is based on MMdetection.

Classification Modeling: Probability of Default

Approaches to modeling terrain and maps in python

Experiments for Fake News explainability project

Deep Reinforcement Learning for Keras.

Generate text captions for images from their CLIP embeddings. Includes PyTorch model code and example training script.

PyTorch implementations of the NeRF model described in "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis"

PyTorch ,ONNX and TensorRT implementation of YOLOv4

Adaptive FNO transformer - official Pytorch implementation

Code for the Shortformer model, from the paper by Ofir Press, Noah A. Smith and Mike Lewis.

GAT - Graph Attention Network (PyTorch) 💻 + graphs + 📣 = ❤️

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)