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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 561–570 of 9051 papers

TitleStatusHype
Diversity-aware Channel Pruning for StyleGAN CompressionCode1
On Pretraining Data Diversity for Self-Supervised LearningCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
DreamDA: Generative Data Augmentation with Diffusion ModelsCode1
Controllable Face Synthesis with Semantic Latent Diffusion ModelsCode1
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised LearningCode1
Scaling Data Diversity for Fine-Tuning Language Models in Human AlignmentCode1
Quality-Diversity Actor-Critic: Learning High-Performing and Diverse Behaviors via Value and Successor Features CriticsCode1
Dyadic Interaction Modeling for Social Behavior GenerationCode1
LAFS: Landmark-based Facial Self-supervised Learning for Face RecognitionCode1
Show:102550
← PrevPage 57 of 906Next →

No leaderboard results yet.