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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 10811090 of 9051 papers

TitleStatusHype
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph ExpertsCode1
InsetGAN for Full-Body Image GenerationCode1
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of RedundancyCode1
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label EnhancementCode1
Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPsCode1
Towards Universal Texture Synthesis by Combining Texton Broadcasting with Noise Injection in StyleGAN-2Code1
Hierarchical Sketch Induction for Paraphrase GenerationCode1
UVCGAN: UNet Vision Transformer cycle-consistent GAN for unpaired image-to-image translationCode1
Polarity Sampling: Quality and Diversity Control of Pre-Trained Generative Networks via Singular ValuesCode1
Biological Sequence Design with GFlowNetsCode1
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