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 221230 of 9051 papers

TitleStatusHype
MultiMedEval: A Benchmark and a Toolkit for Evaluating Medical Vision-Language ModelsCode2
Multi-Space Alignments Towards Universal LiDAR SegmentationCode2
DGR-MIL: Exploring Diverse Global Representation in Multiple Instance Learning for Whole Slide Image ClassificationCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy PerceptionCode2
Bridging Remote Sensors with Multisensor Geospatial Foundation ModelsCode2
Out of Many, One: Designing and Scaffolding Proteins at the Scale of the Structural Universe with Genie 2Code2
Palette: Image-to-Image Diffusion ModelsCode2
PerAct2: Benchmarking and Learning for Robotic Bimanual Manipulation TasksCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
Show:102550
← PrevPage 23 of 906Next →

No leaderboard results yet.