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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 11811190 of 9051 papers

TitleStatusHype
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous DrivingCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Contrastive Syn-to-Real GeneralizationCode1
Dual-stage Hyperspectral Image Classification Model with Spectral SupertokenCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial SensorsCode1
Controllable Multi-Interest Framework for RecommendationCode1
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