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 17311740 of 9051 papers

TitleStatusHype
Multi-Task Curriculum Graph Contrastive Learning with Clustering Entropy Guidance0
Disentangled Structural and Featural Representation for Task-Agnostic Graph Valuation0
MakeupAttack: Feature Space Black-box Backdoor Attack on Face Recognition via Makeup TransferCode0
CARLA Drone: Monocular 3D Object Detection from a Different Perspective0
Interpretable Long-term Action Quality AssessmentCode1
LAHAJA: A Robust Multi-accent Benchmark for Evaluating Hindi ASR SystemsCode0
Cause-Aware Empathetic Response Generation via Chain-of-Thought Fine-Tuning0
SynPlay: Importing Real-world Diversity for a Synthetic Human Dataset0
Lookism: The overlooked bias in computer vision0
Mechanics promotes coherence in heterogeneous active media0
Show:102550
← PrevPage 174 of 906Next →

No leaderboard results yet.