Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

Overview

Can Active Learning Preemptively Mitigate Fairness Issues?

Code for the paper "Can Active Learning Preemptively Mitigate Fairness Issues?" presented at RAI 2021.

Arxiv link: https://arxiv.org/pdf/2104.06879.pdf

This repo aims at helping researchers reproduce our paper. We welcome questions and suggestions, please submit an issue! If you are working in active learning, our library BaaL might help you!

Glossary

  • query_size : Number of data labelled per AL step
  • AL Step: process of training, selecting samples, and labelling.
  • learning_epoch: Number of epoch to train the model before uncertainty estimation.

Datasets

We experimented with many datasets and we have one format.

# For synbols datasets
with open(dataset_path, 'rb') as f:
    train_ds = pickle.load(f)
    test_ds = pickle.load(f)
    val_ds = pickle.load(f)


x,y = train_ds
print(x[0])
# A numpy array with shape [64,64,3]

print(y[0])
# A dictionnary with all the attributes and keys.
"""
{'char':'a',
 'color': 'r',
 ...
}
"""

Models

We use a VGG-16 and we do AL with MC-Dropout.

from active_fairness import utils
from baal.bayesian.dropout import patch_module

hyperparams = {} # Dictionnary for the hparams

model = utils.vgg16(pretrained=hyperparams['pretrained'],
                          num_classes=hyperparams['n_cls'])

# change dropout layer to be able to use MC-Dropout
model = patch_module(model)

How to launch

Here is an example to run our experiment on a particular dataset.

To generate the datasets, please look in misc/biased_dataset.ipynb.

poetry run python experiments/grad_experiment.py datasets/minority_dataset_50000.pkl color char

Owner
ElementAI
ElementAI
A pure PyTorch implementation of the loss described in "Online Segment to Segment Neural Transduction"

ssnt-loss ℹ️ This is a WIP project. the implementation is still being tested. A pure PyTorch implementation of the loss described in "Online Segment t

張致強 1 Feb 09, 2022
PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud, CVPR 2019.

PointRCNN PointRCNN: 3D Object Proposal Generation and Detection from Point Cloud Code release for the paper PointRCNN:3D Object Proposal Generation a

Shaoshuai Shi 1.5k Dec 27, 2022
Source codes of CenterTrack++ in 2021 ICME Workshop on Big Surveillance Data Processing and Analysis

MOT Tracked object bounding box association (CenterTrack++) New association method based on CenterTrack. Two new branches (Tracked Size and IOU) are a

36 Oct 04, 2022
social humanoid robots with GPGPU and IoT

Social humanoid robots with GPGPU and IoT Social humanoid robots with GPGPU and IoT Paper Authors Mohsen Jafarzadeh, Stephen Brooks, Shimeng Yu, Balak

0 Jan 07, 2022
Automated Evidence Collection for Fake News Detection

Automated Evidence Collection for Fake News Detection This is the code repo for the Automated Evidence Collection for Fake News Detection paper accept

Mrinal Rawat 2 Apr 12, 2022
Additional environments compatible with OpenAI gym

Decentralized Control of Quadrotor Swarms with End-to-end Deep Reinforcement Learning A codebase for training reinforcement learning policies for quad

Zhehui Huang 40 Dec 06, 2022
RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids

RMTD: Robust Moving Target Defence Against False Data Injection Attacks in Power Grids Real-time detection performance. This repo contains the code an

0 Nov 10, 2021
This is the implementation of our work Deep Extreme Cut (DEXTR), for object segmentation from extreme points.

This is the implementation of our work Deep Extreme Cut (DEXTR), for object segmentation from extreme points.

Sergi Caelles 828 Jan 05, 2023
PyTorch Implementation for AAAI'21 "Do Response Selection Models Really Know What's Next? Utterance Manipulation Strategies for Multi-turn Response Selection"

UMS for Multi-turn Response Selection Implements the model described in the following paper Do Response Selection Models Really Know What's Next? Utte

Taesun Whang 47 Nov 22, 2022
Clockwork Variational Autoencoder

Clockwork Variational Autoencoders (CW-VAE) Vaibhav Saxena, Jimmy Ba, Danijar Hafner If you find this code useful, please reference in your paper: @ar

Vaibhav Saxena 35 Nov 06, 2022
MBPO (paper: When to trust your model: Model-based policy optimization) in offline RL settings

offline-MBPO This repository contains the code of a version of model-based RL algorithm MBPO, which is modified to perform in offline RL settings Pape

LxzGordon 1 Oct 24, 2021
Face-Recognition-Attendence-System - This face recognition Attendence system using Python

Face-Recognition-Attendence-System I have developed this face recognition Attend

Riya Gupta 4 May 10, 2022
ThunderSVM: A Fast SVM Library on GPUs and CPUs

What's new We have recently released ThunderGBM, a fast GBDT and Random Forest library on GPUs. add scikit-learn interface, see here Overview The miss

Xtra Computing Group 1.4k Dec 22, 2022
Research on controller area network Intrusion Detection Systems

Group members information Member 1: Lixue Liang Member 2: Yuet Lee Chan Member 3: Xinruo Zhang Member 4: Yifei Han User Manual Generate Attack Packets

Roche 4 Aug 30, 2022
Dense Prediction Transformers

Vision Transformers for Dense Prediction This repository contains code and models for our paper: Vision Transformers for Dense Prediction René Ranftl,

Intel ISL (Intel Intelligent Systems Lab) 1.3k Dec 28, 2022
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation

SUO-SLAM This repository hosts the code for our CVPR 2022 paper "Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation". ArXiv li

Robot Perception & Navigation Group (RPNG) 97 Jan 03, 2023
[NeurIPS 2021] "Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks" by Yonggan Fu, Qixuan Yu, Yang Zhang, Shang Wu, Xu Ouyang, David Cox, Yingyan Lin

Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks Yonggan Fu, Qixuan Yu, Yang Zhang, S

12 Dec 11, 2022
Instance Segmentation by Jointly Optimizing Spatial Embeddings and Clustering Bandwidth

Instance segmentation by jointly optimizing spatial embeddings and clustering bandwidth This codebase implements the loss function described in: Insta

209 Dec 07, 2022
Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Learning with Nonignorable Nonresponses‘

Graph-based joint model with Nonignorable Missingness (GNM) This is a Keras implementation of the GNM model in paper ’Graph-Based Semi-Supervised Lear

Fan Zhou 2 Apr 17, 2022
My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs (GNN, GAT, GraphSAGE, GCN)

machine-learning-with-graphs My solutions for Stanford University course CS224W: Machine Learning with Graphs Fall 2021 colabs Course materials can be

Marko Njegomir 7 Dec 14, 2022