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 19111920 of 9051 papers

TitleStatusHype
Learning Semantic Latent Directions for Accurate and Controllable Human Motion PredictionCode1
Data-Juicer Sandbox: A Comprehensive Suite for Multimodal Data-Model Co-development0
CIC-BART-SSA: Controllable Image Captioning with Structured Semantic AugmentationCode0
CCVA-FL: Cross-Client Variations Adaptive Federated Learning for Medical Imaging0
Repurformer: Transformers for Repurposing-Aware Molecule Generation0
Don't Throw Away Data: Better Sequence Knowledge Distillation0
Omni-Dimensional Frequency Learner for General Time Series Analysis0
Domain Generalization for 6D Pose Estimation Through NeRF-based Image Synthesis0
Towards Enhanced Classification of Abnormal Lung sound in Multi-breath: A Light Weight Multi-label and Multi-head Attention Classification Method0
DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion ModelsCode1
Show:102550
← PrevPage 192 of 906Next →

No leaderboard results yet.