Papers with Code 아카이브

Fairness

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

1

평가표

9

보존된 평가 기록

10

코드

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

https://github.com/Nellaker-group/FairUnsupervisedRepresentations

Learning to Pivot with Adversarial Networks

https://github.com/OanaMariaCamburu/e-SNLI

The Struggles of Feature-Based Explanations: Shapley Values vs. Minimal Sufficient Subsets

https://github.com/PaddlePaddle/PaddleDetection

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/PietroMelzi/GANDiffFace

Synthetic Data for the Mitigation of Demographic Biases in Face Recognition

https://github.com/RUCAIBox/RecBole

RecBole 2.0: Towards a More Up-to-Date Recommendation Library

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

Fairness in Face Presentation Attack Detection

https://github.com/RonyAbecidan/PrivateWordEmbeddings

Differentially Private Representation for NLP: Formal Guarantee and An Empirical Study on Privacy and Fairness

https://github.com/SJabin/Data_Model_Dependencies_MIA

Disparate Vulnerability to Membership Inference Attacks

https://github.com/SaeedSharifiMa/FairDP

Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness

https://github.com/SauravMaheshkar/Compressed-DNNs-Forget

What Do Compressed Deep Neural Networks Forget?

https://github.com/SavvinaDaniil/UnfairnessOfPopularityBias

The Unfairness of Popularity Bias in Book Recommendation

https://github.com/Shaked35/fairness-optimization

Beyond Parity: Fairness Objectives for Collaborative Filtering

https://github.com/Shivamm14/SRIP

Ranking with Fairness Constraints

https://github.com/Stanford-AIMI/CheXagent

A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation

https://github.com/TiTIAi/Block_chain

Blockchain-empowered Federated Learning: Benefits, Challenges, and Solutions

https://github.com/TsingZ0/PFL-Non-IID

Ditto: Fair and Robust Federated Learning Through Personalization

https://github.com/TsingZ0/PFLlib

GPFL: Simultaneously Learning Global and Personalized Feature Information for Personalized Federated Learning

https://github.com/ZJUT-ERCISS/zjut_mindvideo/blob/master/msvideo/models/fairmot.py

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/aai-institute/pyDVL

Efficient Task-Specific Data Valuation for Nearest Neighbor Algorithms

https://github.com/abbaasalif/freematch-improved

FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

https://github.com/abbie0214/Fairness-without-Demographics-through-Adversarially-Reweighted-Learning

Fairness without Demographics through Adversarially Reweighted Learning

https://github.com/aimat-lab/graph_attention_student

Global Concept Explanations for Graphs by Contrastive Learning

https://github.com/alangee/FaiR-N

FaiR-N: Fair and Robust Neural Networks for Structured Data

https://github.com/amazon-research/minimax-fair

Minimax Group Fairness: Algorithms and Experiments

https://github.com/amazon-science/fair-pca

Convex Formulations for Fair Principal Component Analysis

https://github.com/amazon-science/generalized-fairness-metrics

Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics

https://github.com/amiratag/multiaccuracyboost

Multiaccuracy: Black-Box Post-Processing for Fairness in Classification

https://github.com/anaymehrotra/ranking-with-implicit-bias

Mitigating Bias in Set Selection with Noisy Protected Attributes

https://github.com/andreysorokin/scam-net

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

https://github.com/ankitsinghsuraj/mot20

FairMOT: On the Fairness of Detection and Re-Identification in Multiple Object Tracking

https://github.com/anonymous12138/biasmitigation

Bias in Machine Learning Software: Why? How? What to do?

https://github.com/apple/ml-delphi

DELPHI: Data for Evaluating LLMs' Performance in Handling Controversial Issues

https://github.com/aradhanacha/aif360

Classification with Fairness Constraints: A Meta-Algorithm with Provable Guarantees

https://github.com/arezae4/fair-logloss-classification

Fairness for Robust Log Loss Classification

https://github.com/arezoosarvi/omit_fair_ranking

Understanding and Mitigating the Effect of Outliers in Fair Ranking

https://github.com/asheshrambachan/Fairness_In_The_Rashomon_Set

Characterizing Fairness Over the Set of Good Models Under Selective Labels

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