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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 431440 of 9051 papers

TitleStatusHype
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal TransferCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Deep generative selection models of T and B cell receptor repertoires with soNNiaCode1
DELT: A Simple Diversity-driven EarlyLate Training for Dataset DistillationCode1
Active learning for medical image segmentation with stochastic batchesCode1
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose EstimationCode1
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