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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 101110 of 9051 papers

TitleStatusHype
DualAnoDiff: Dual-Interrelated Diffusion Model for Few-Shot Anomaly Image GenerationCode2
Efficient Quality Diversity Optimization of 3D Buildings through 2D Pre-optimizationCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
Ambiguous Medical Image Segmentation using Diffusion ModelsCode2
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
DPOT: Auto-Regressive Denoising Operator Transformer for Large-Scale PDE Pre-TrainingCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
EDGE: Editable Dance Generation From MusicCode2
Diffusion Models Beat GANs on Image SynthesisCode2
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