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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 18311840 of 9051 papers

TitleStatusHype
S-SYNTH: Knowledge-Based, Synthetic Generation of Skin ImagesCode0
PhysFlow: Skin tone transfer for remote heart rate estimation through conditional normalizing flows0
Expressive Whole-Body 3D Gaussian AvatarCode4
WAS: Dataset and Methods for Artistic Text SegmentationCode1
MOSAIC: Multimodal Multistakeholder-aware Visual Art Recommendation0
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer CapabilitiesCode1
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
Toward Wireless Localization Using Multiple Reconfigurable Intelligent Surfaces0
Monocular Human-Object Reconstruction in the WildCode1
Efficient Data Representation for Motion Forecasting: A Scene-Specific Trajectory Set Approach0
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