SOTAVerified

Diversity

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.

Papers

Showing 151160 of 9051 papers

TitleStatusHype
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
3DGen: Triplane Latent Diffusion for Textured Mesh GenerationCode2
Diffusion Bridge Implicit ModelsCode2
A Closer Look into Mixture-of-Experts in Large Language ModelsCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Show:102550
← PrevPage 16 of 906Next →

No leaderboard results yet.