Papers with Code 아카이브

Diversity

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

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/LAIT-CVLab/TopPR

공식 코드

TopP&R: Robust Support Estimation Approach for Evaluating Fidelity and Diversity in Generative Models

https://github.com/LAL/trackml-library

공식 코드

The Tracking Machine Learning challenge : Throughput phase

https://github.com/LTL2Action/LTL2Action

공식 코드

LTL2Action: Generalizing LTL Instructions for Multi-Task RL

https://github.com/LUSSeg/ImageNet-S

공식 코드

Large-scale Unsupervised Semantic Segmentation

https://github.com/LarsHill/pointer-guided-pre-training

공식 코드

Pointer-Guided Pre-Training: Infusing Large Language Models with Paragraph-Level Contextual Awareness

https://github.com/LazarusNLP/NusaBERT

공식 코드

NusaBERT: Teaching IndoBERT to be Multilingual and Multicultural

https://github.com/LeapLabTHU/diver-ct

공식 코드

DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing Constraints

https://github.com/Leminhbinh0209/KappaFace

공식 코드

KappaFace: Adaptive Additive Angular Margin Loss for Deep Face Recognition

https://github.com/LequnWang/Improve-Screening-via-Calibrated-Subset-Selection

공식 코드

Improving Screening Processes via Calibrated Subset Selection

https://github.com/Letian2003/MM_INF

공식 코드

Oasis: One Image is All You Need for Multimodal Instruction Data Synthesis

https://github.com/LiQisResearch/KDD--RF-MA3S

공식 코드

Diversity Optimization for Travelling Salesman Problem via Deep Reinforcement Learning

https://github.com/Liang-ZX/adaptdiffuser

공식 코드

AdaptDiffuser: Diffusion Models as Adaptive Self-evolving Planners

https://github.com/LinShiqi047/PatchAutoAugment

공식 코드

Local Patch AutoAugment with Multi-Agent Collaboration

https://github.com/LinusWu/TENET_Training

공식 코드

Group-wise Inhibition based Feature Regularization for Robust Classification

https://github.com/LxzGordon/pecan_human_AI_coordination

공식 코드

PECAN: Leveraging Policy Ensemble for Context-Aware Zero-Shot Human-AI Coordination

https://github.com/M-Elfeki/GDPP

공식 코드

GDPP: Learning Diverse Generations Using Determinantal Point Process

https://github.com/MECLabTUDA/VoxelSceneGraph

공식 코드

Federated Voxel Scene Graph for Intracranial Hemorrhage

https://github.com/MIPT-Oulu/greedy_ensembles_training

공식 코드

Greedy Bayesian Posterior Approximation with Deep Ensembles

https://github.com/MJJdG/ULS23

공식 코드

The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography

https://github.com/MSD-IRIMAS/Evaluating-HMG

공식 코드

Establishing a Unified Evaluation Framework for Human Motion Generation: A Comparative Analysis of Metrics

https://github.com/MajidMoghadam2006/RL-frenet-trajectory-planning-in-CARLA

공식 코드

An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet Space

https://github.com/Maluuba/nlg-eval

공식 코드

Relevance of Unsupervised Metrics in Task-Oriented Dialogue for Evaluating Natural Language Generation

https://github.com/MarkWuNLP/ResponseEdit

공식 코드

Response Generation by Context-aware Prototype Editing

https://github.com/MaximeGirard/once-for-all-memory-constant

공식 코드

Memory-Optimized Once-For-All Network

https://github.com/MedMCQA/MedMCQA

공식 코드

MedMCQA : A Large-scale Multi-Subject Multi-Choice Dataset for Medical domain Question Answering

https://github.com/MengzhangLI/ASNG

공식 코드

Automatic Data Augmentation for 3D Medical Image Segmentation

https://github.com/Meowuu7/few-arti-gen

공식 코드

Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical Deformation

https://github.com/MetaMain/BewareAdvML

공식 코드

Beware the Black-Box: on the Robustness of Recent Defenses to Adversarial Examples

https://github.com/MilesZhao/PGCGM

공식 코드

Physics Guided Deep Learning for Generative Design of Crystal Materials with Symmetry Constraints

https://github.com/MiuLab/TaylorGAN

공식 코드

TaylorGAN: Neighbor-Augmented Policy Update for Sample-Efficient Natural Language Generation

https://github.com/MorenoLaQuatra/ARCH

공식 코드

Benchmarking Representations for Speech, Music, and Acoustic Events

https://github.com/MyeongJin-Kim/Learning-Texture-Invariant-Representation

공식 코드

Learning Texture Invariant Representation for Domain Adaptation of Semantic Segmentation

https://github.com/NLP2CT/Unsupervised_Dialect_Translation

공식 코드

Unsupervised Neural Dialect Translation with Commonality and Diversity Modeling

https://github.com/NVlabs/CSG

공식 코드

Contrastive Syn-to-Real Generalization

https://github.com/NVlabs/DeepInversion

공식 코드

Dreaming to Distill: Data-free Knowledge Transfer via DeepInversion

https://github.com/NVlabs/FoundationStereo

공식 코드

FoundationStereo: Zero-Shot Stereo Matching

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