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 741750 of 9051 papers

TitleStatusHype
PlatoLM: Teaching LLMs in Multi-Round Dialogue via a User SimulatorCode1
Few-Shot Physically-Aware Articulated Mesh Generation via Hierarchical DeformationCode1
TransFace: Calibrating Transformer Training for Face Recognition from a Data-Centric PerspectiveCode1
MDCS: More Diverse Experts with Consistency Self-distillation for Long-tailed RecognitionCode1
Diverse Cotraining Makes Strong Semi-Supervised SegmentorCode1
LaRS: A Diverse Panoptic Maritime Obstacle Detection Dataset and BenchmarkCode1
Language-guided Human Motion Synthesis with Atomic ActionsCode1
OpenGCD: Assisting Open World Recognition with Generalized Category DiscoveryCode1
Compositional Feature Augmentation for Unbiased Scene Graph GenerationCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Show:102550
← PrevPage 75 of 906Next →

No leaderboard results yet.