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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 481490 of 9051 papers

TitleStatusHype
Analysis of diversity-accuracy tradeoff in image captioningCode1
Don't Touch What Matters: Task-Aware Lipschitz Data Augmentation for Visual Reinforcement LearningCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided DiffusionCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Aligning Language Models with Preferences through f-divergence MinimizationCode1
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
DriveDiTFit: Fine-tuning Diffusion Transformers for Autonomous DrivingCode1
Effective Diversity in Population Based Reinforcement LearningCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
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