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 11111120 of 9051 papers

TitleStatusHype
Towards Objective Metrics for Procedurally Generated Video Game LevelsCode1
WANLI: Worker and AI Collaboration for Natural Language Inference Dataset CreationCode1
Tailor Versatile Multi-modal Learning for Multi-label Emotion RecognitionCode1
Realistic Full-Body Anonymization with Surface-Guided GANsCode1
To miss-attend is to misalign! Residual Self-Attentive Feature Alignment for Adapting Object DetectorsCode1
PoseTrack21: A Dataset for Person Search, Multi-Object Tracking and Multi-Person Pose TrackingCode1
Style-Structure Disentangled Features and Normalizing Flows for Diverse Icon ColorizationCode1
Exploring Effective Data for Surrogate Training Towards Black-Box AttackCode1
Image Disentanglement Autoencoder for Steganography Without EmbeddingCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
Show:102550
← PrevPage 112 of 906Next →

No leaderboard results yet.