Papers with Code 아카이브
Diversity
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 0
- 보존된 평가 기록
- 0
Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/Sharif-SLPL/t5-fa
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
https://github.com/SharmaAjay19/VideoImageColorization
https://github.com/Shivamm14/SRIP
https://github.com/SiamakGhodsi/SPMLD
https://github.com/SimonKohl/probabilistic_unet
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale Ambiguities
https://github.com/SimonThomine/DistillationAD
Anomaly Detection via Reverse Distillation from One-Class Embedding
https://github.com/SimonThomine/RememberingNormality
Anomaly Detection via Reverse Distillation from One-Class Embedding
https://github.com/SoftwareGift/FeatheNets_Face-Anti-spoofing-Attack-Detection-Challenge-CVPR2019
FeatherNets: Convolutional Neural Networks as Light as Feather for Face Anti-spoofing
https://github.com/StatNLP/ada4asr
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/StatNLP/joeynmt
Participatory Research for Low-resourced Machine Translation: A Case Study in African Languages
https://github.com/SumUpAnalytics/goldsum
Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
https://github.com/SunJiamei/LRP-imagecaptioning-pytorch
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence Models
https://github.com/Sunkua/Colorizer
https://github.com/SuperbTUM/REID-IDM
IDM: An Intermediate Domain Module for Domain Adaptive Person Re-ID
https://github.com/THUDM/GATNE
https://github.com/THUDM/GLM
The Pile: An 800GB Dataset of Diverse Text for Language Modeling
https://github.com/THUDM/KOBE
https://github.com/THUcqb/KOBE
https://github.com/TRI-ML/vidar
Depth Field Networks for Generalizable Multi-view Scene Representation
https://github.com/TaiseiAso/BiGruAttEncDec
A Simple, Fast Diverse Decoding Algorithm for Neural Generation
https://github.com/TakaOsa/LTD3
https://github.com/ThierryBarros/reproduction-textrank
https://github.com/ThomasNorr/QPM
Take 5: Interpretable Image Classification with a Handful of Features
https://github.com/TianheWu/LGPNet
https://github.com/TingchenFu/ACL24-ExpertFusion
Explore-Instruct: Enhancing Domain-Specific Instruction Coverage through Active Exploration
https://github.com/Tixierae/gow_tools
https://github.com/TonyNemo/UBAR-MultiWOZ
Task-Oriented Dialog Systems that Consider Multiple Appropriate Responses under the Same Context
https://github.com/Trustworthy-AI-Group/TransferAttack
Improving Transferability of Adversarial Examples with Input Diversity
https://github.com/TsingZ0/PFL-Non-IID
https://github.com/USTCzzl/Deecamp27
https://github.com/UdonDa/StarGAN-v2-pytorch-nonofficial
https://github.com/UladzimirBaklan/qwerty
A Diversity-Promoting Objective Function for Neural Conversation Models
https://github.com/UnofficialJuliaMirror/Diversity.jl-d3d5718d-52de-57ab-b67a-eca7fd6175a4
https://github.com/UnofficialJuliaMirrorSnapshots/Diversity.jl-d3d5718d-52de-57ab-b67a-eca7fd6175a4
https://github.com/Vill-Lab/forkOfAlchemistCoder
AlchemistCoder: Harmonizing and Eliciting Code Capability by Hindsight Tuning on Multi-source Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.