Papers with Code 아카이브
Fairness
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/sonyresearch/apparent_skincolor
Beyond Skin Tone: A Multidimensional Measure of Apparent Skin Color
https://github.com/sooeun67/xai
https://github.com/sreedhr92/Covid-19-Prediction-using-Chest-X-rays
A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
https://github.com/sruthigorantla/underranking_and_group_fairness
https://github.com/steven7woo/fair_regression_reduction
Fair Regression: Quantitative Definitions and Reduction-based Algorithms
https://github.com/sundave1998/independent-npg-mpg
Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning
https://github.com/tabayashi0117/Score-CAM
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
https://github.com/taejun13/TransferLearning_VerifyFairness
Learning Adversarially Fair and Transferable Representations
https://github.com/thoppe/Twitter-Ethics-Challenge-PixelPerfect
https://github.com/tianxiangzhao/fairlearn
Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features
https://github.com/tj-kim/pfeddef_v1
Federated Multi-Task Learning under a Mixture of Distributions
https://github.com/torchjd/torchjd
https://github.com/trailofbits/privacyraven
https://github.com/uiuc-arc/incremental-dnn-verification
https://github.com/umersheikh846/WQMIX
Learning Fair Policies in Decentralized Cooperative Multi-Agent Reinforcement Learning
https://github.com/understanding-ml/COLA
Global Counterfactual Explanations: Investigations, Implementations and Improvements
https://github.com/upunaprosk/fair-pruning
Nuanced Metrics for Measuring Unintended Bias with Real Data for Text Classification
https://github.com/vanderschaarlab/datagnosis
Synthcity: facilitating innovative use cases of synthetic data in different data modalities
https://github.com/vardigroup/DPMC
https://github.com/vijaykeswani/FairClassification
Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees
https://github.com/vijaykeswani/Noisy-Fair-Classification
Fair Classification with Noisy Protected Attributes: A Framework with Provable Guarantees
https://github.com/vijaykeswani/closed-loop-task-allocation
https://github.com/vincent-grari/FAGTB
https://github.com/vitaly-emelianov/multistage_fairness
https://github.com/voidism/invrat_debias
Challenges in Automated Debiasing for Toxic Language Detection
https://github.com/wangyongjie-ntu/CFAI
https://github.com/wangyongjie-ntu/Counterfactual-Explanations-Pytorch
https://github.com/wannaa/c-flat
Maintaining Discrimination and Fairness in Class Incremental Learning
https://github.com/webis-de/acl-24
Disentangling Dialect from Social Bias via Multitask Learning to Improve Fairness
https://github.com/wibrown/GerryFair
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
https://github.com/wiebket/bt4vt
SVEva Fair: A Framework for Evaluating Fairness in Speaker Verification
https://github.com/willtop/Spatial_DeepLearning_Wireless_Scheduling
https://github.com/windstormer/Cfd-CAM
Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks
https://github.com/wtliao/FairNN
FairNN- Conjoint Learning of Fair Representations for Fair Decisions
https://github.com/xiaomi-automl/FairNAS
FairNAS: Rethinking Evaluation Fairness of Weight Sharing Neural Architecture Search
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.