Papers with Code 아카이브

Fairness

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/dmadras/predict-responsibly

Predict Responsibly: Improving Fairness and Accuracy by Learning to Defer

https://github.com/dns43/fairness

Achieving Fairness through Adversarial Learning: an Application to Recidivism Prediction

https://github.com/dr-aheydari/SoftAdapt

Going Beyond Classification Accuracy Metrics in Model Compression

https://github.com/dragonsan17/covid_detection_from_xray

A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images

https://github.com/dreji18/fairness-in-ai

An Approach to Ensure Fairness in News Articles

https://github.com/ecreager/laftr

Learning Adversarially Fair and Transferable Representations

https://github.com/elsalmi/LendingClub

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/enouenj/sparse-interaction-additive-networks

NODE-GAM: Neural Generalized Additive Model for Interpretable Deep Learning

https://github.com/eric-ai-lab/pevit

ELEVATER: A Benchmark and Toolkit for Evaluating Language-Augmented Visual Models

https://github.com/exploring-curiosity/CovidPredictionML

A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images

https://github.com/facebookresearch/disentangling-correlated-factors

Weakly-Supervised Disentanglement Without Compromises

https://github.com/fair-search/fairsearch-fair-python

FA*IR: A Fair Top-k Ranking Algorithm

https://github.com/fairfl/FCFL

Agnostic Federated Learning

https://github.com/fairfl/FUEL

Agnostic Federated Learning

https://github.com/fairlearn/fairlearn

A Reductions Approach to Fair Classification

https://github.com/fairness-adversarial/unbiased_representations_renyi

Learning Unbiased Representations via Rényi Minimization

https://github.com/faroukmokhtar/DESY

Learning to Pivot with Adversarial Networks

https://github.com/faroukmokhtar/DESYproject

Learning to Pivot with Adversarial Networks

https://github.com/faroukmokhtar/GradProject

Learning to Pivot with Adversarial Networks

https://github.com/ferryjul/fairCORELS

Learning Fair Rule Lists

https://github.com/florenciopaucar/Covid-DataBoss

A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images

https://github.com/for-ai/llm-profiling-toolkit

Holistic Evaluation of Language Models

https://github.com/frgfm/torch-cam

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

https://github.com/ftramer/lm-extraction-benchmark

Quantifying Memorization Across Neural Language Models

https://github.com/g-u-n/pycil

Maintaining Discrimination and Fairness in Class Incremental Learning

https://github.com/galdeia/feat

Genetic programming approaches to learning fair classifiers

https://github.com/gaur-pankaj/Covid19-Detection

A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images

https://github.com/genforce/fairgen

Improving the Fairness of Deep Generative Models without Retraining

https://github.com/giandos200/Zhang-et-al-Mitigating-Unwanted-Biases-with-Adversarial-Learning

Mitigating Unwanted Biases with Adversarial Learning

https://github.com/gitforreview/dsaa_journal_review

AI Fairness 360: An Extensible Toolkit for Detecting, Understanding, and Mitigating Unwanted Algorithmic Bias

https://github.com/glouppe/paper-learning-to-pivot

Learning to Pivot with Adversarial Networks

https://github.com/google-deepmind/jax_privacy

Unlocking Accuracy and Fairness in Differentially Private Image Classification

https://github.com/google-research/lm-extraction-benchmark

Quantifying Memorization Across Neural Language Models

https://github.com/google-research/tensorflow_constrained_optimization

Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints

https://github.com/google/uncertainty-baselines

Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification

https://github.com/gourabkumarpatro/fairrec

FairRec: Two-Sided Fairness for Personalized Recommendations in Two-Sided Platforms

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.