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 21212130 of 9051 papers

TitleStatusHype
StableMaterials: Enhancing Diversity in Material Generation via Semi-Supervised Learning0
FouRA: Fourier Low Rank Adaptation0
A Large-scale Universal Evaluation Benchmark For Face Forgery DetectionCode1
FairCoT: Enhancing Fairness in Diffusion Models via Chain of Thought Reasoning of Multimodal Language Models0
MMRel: A Relation Understanding Benchmark in the MLLM EraCode1
Hallo: Hierarchical Audio-Driven Visual Synthesis for Portrait Image AnimationCode9
Pareto Front-Diverse Batch Multi-Objective Bayesian OptimizationCode0
Depth Anything V2Code9
Unraveling Code-Mixing Patterns in Migration Discourse: Automated Detection and Analysis of Online Conversations on RedditCode0
Data Augmentation by Fuzzing for Neural Test Generation0
Show:102550
← PrevPage 213 of 906Next →

No leaderboard results yet.