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 341350 of 9051 papers

TitleStatusHype
Dataset Factorization for CondensationCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Active Teacher for Semi-Supervised Object DetectionCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Dan: Deep attention neural network for news recommendationCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Show:102550
← PrevPage 35 of 906Next →

No leaderboard results yet.