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 861870 of 9051 papers

TitleStatusHype
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
Active Teacher for Semi-Supervised Object DetectionCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
Diversity-Aware Meta Visual PromptingCode1
RiDDLE: Reversible and Diversified De-identification with Latent EncryptorCode1
Semi-Federated Learning for Collaborative Intelligence in Massive IoT NetworksCode1
Diversity-Measurable Anomaly DetectionCode1
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous DrivingCode1
SemEval-2023 Task 10: Explainable Detection of Online SexismCode1
Show:102550
← PrevPage 87 of 906Next →

No leaderboard results yet.