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 10911100 of 9051 papers

TitleStatusHype
Self-Supervised Vision Transformers Learn Visual Concepts in HistopathologyCode1
Submodlib: A Submodular Optimization LibraryCode1
VLAD-VSA: Cross-Domain Face Presentation Attack Detection with Vocabulary Separation and AdaptationCode1
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement LearningCode1
Realistic Blur Synthesis for Learning Image DeblurringCode1
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style TransformationCode1
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillationCode1
Approximating Gradients for Differentiable Quality Diversity in Reinforcement LearningCode1
Red Teaming Language Models with Language ModelsCode1
Show:102550
← PrevPage 110 of 906Next →

No leaderboard results yet.