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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

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Showing 3761–3770 of 9051 papers

TitleStatusHype
Diverse Part Discovery: Occluded Person Re-identification with Part-Aware Transformer—0
Generative Data Augmentation Challenge: Synthesis of Room Acoustics for Speaker Distance Estimation—0
Flow Score Distillation for Diverse Text-to-3D Generation—0
Fluctuating growth rates link turnover and unevenness in species-rich communities—0
Boosting Diffusion Model for Spectrogram Up-sampling in Text-to-speech: An Empirical Study—0
FMiFood: Multi-modal Contrastive Learning for Food Image Classification—0
FM Tone Transfer with Envelope Learning—0
Focus Attention: Promoting Faithfulness and Diversity in Summarization—0
Focus-Consistent Multi-Level Aggregation for Compositional Zero-Shot Learning—0
Offline Diversity Maximization Under Imitation Constraints—0
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