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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 801810 of 9051 papers

TitleStatusHype
Stochastic Pitch Prediction Improves the Diversity and Naturalness of Speech in Glow-TTSCode1
Rethinking Masked Language Modeling for Chinese Spelling CorrectionCode1
Exploiting Abstract Meaning Representation for Open-Domain Question AnsweringCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Diversify Your Vision Datasets with Automatic Diffusion-Based AugmentationCode1
Self-aware and Cross-sample Prototypical Learning for Semi-supervised Medical Image SegmentationCode1
ProlificDreamer: High-Fidelity and Diverse Text-to-3D Generation with Variational Score DistillationCode1
Optimized Custom Dataset for Efficient Detection of Underwater TrashCode1
CommonScenes: Generating Commonsense 3D Indoor Scenes with Scene Graph DiffusionCode1
CodeInstruct: Empowering Language Models to Edit CodeCode1
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