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 281290 of 9051 papers

TitleStatusHype
Depth Field Networks for Generalizable Multi-view Scene RepresentationCode2
EgoMimic: Scaling Imitation Learning via Egocentric VideoCode2
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single ImageCode2
Enhancing Sample Efficiency and Exploration in Reinforcement Learning through the Integration of Diffusion Models and Proximal Policy OptimizationCode2
EVA3D: Compositional 3D Human Generation from 2D Image CollectionsCode2
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous SpeechCode2
DeepPrivacy2: Towards Realistic Full-Body AnonymizationCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
Show:102550
← PrevPage 29 of 906Next →

No leaderboard results yet.