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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 32613270 of 9051 papers

TitleStatusHype
Crowd Access Path Optimization: Diversity Matters0
Augmented Conditioning Is Enough For Effective Training Image Generation0
Align Your Rhythm: Generating Highly Aligned Dance Poses with Gating-Enhanced Rhythm-Aware Feature Representation0
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-Speech0
Augment, Drop & Swap: Improving Diversity in LLM Captions for Efficient Music-Text Representation Learning0
Cross-Modality Person Re-Identification via Modality Confusion and Center Aggregation0
Augmentation with Projection: Towards an Effective and Efficient Data Augmentation Paradigm for Distillation0
Cross-modal Face- and Voice-style Transfer0
Cross-Lingual Transfer of Cultural Knowledge: An Asymmetric Phenomenon0
Cross-Layer Strategic Ensemble Defense Against Adversarial Examples0
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