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 261270 of 9051 papers

TitleStatusHype
BITS: Bi-level Imitation for Traffic SimulationCode2
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
Classifier-Free Diffusion GuidanceCode2
CelebV-HQ: A Large-Scale Video Facial Attributes DatasetCode2
Omni3D: A Large Benchmark and Model for 3D Object Detection in the WildCode2
Large Scale Radio Frequency Signal ClassificationCode2
Pretraining a Neural Network before Knowing Its ArchitectureCode2
Unsupervised Medical Image Translation with Adversarial Diffusion ModelsCode2
BigBIO: A Framework for Data-Centric Biomedical Natural Language ProcessingCode2
Semantic Image Synthesis via Diffusion ModelsCode2
Show:102550
← PrevPage 27 of 906Next →

No leaderboard results yet.