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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 3201–3210 of 9051 papers

TitleStatusHype
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning—0
Touring sampling with pushforward maps—0
XAGen: 3D Expressive Human Avatars Generation—0
DiverseNet: Decision Diversified Semi-supervised Semantic Segmentation Networks for Remote Sensing Imagery—0
Multi-modal In-Context Learning Makes an Ego-evolving Scene Text RecognizerCode1
Recognition-Guided Diffusion Model for Scene Text Image Super-Resolution—0
Data Diversity Matters for Robust Instruction Tuning—0
Revisiting the Domain Shift and Sample Uncertainty in Multi-source Active Domain Transfer—0
Infinite forecast combinations based on Dirichlet process—0
Fin-QD: A Computational Design Framework for Soft Grippers: Integrating MAP-Elites and High-fidelity FEM—0
Show:102550
← PrevPage 321 of 906Next →

No leaderboard results yet.