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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 13111320 of 9051 papers

TitleStatusHype
GS-Blur: A 3D Scene-Based Dataset for Realistic Image DeblurringCode1
High-Fidelity Pluralistic Image Completion with TransformersCode1
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation FrameworkCode1
CAPIVARA: Cost-Efficient Approach for Improving Multilingual CLIP Performance on Low-Resource LanguagesCode1
Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose EstimationCode1
GIQA: Generated Image Quality AssessmentCode1
Can we use Common Voice to train a Multi-Speaker TTS system?Code1
Global Tensor Motion PlanningCode1
Can pre-trained models assist in dataset distillation?Code1
Gesture2Vec: Clustering Gestures using Representation Learning Methods for Co-speech Gesture GenerationCode1
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