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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 111120 of 9051 papers

TitleStatusHype
EVA3D: Compositional 3D Human Generation from 2D Image CollectionsCode2
EvalGIM: A Library for Evaluating Generative Image ModelsCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
DivPrune: Diversity-based Visual Token Pruning for Large Multimodal ModelsCode2
AEM: Attention Entropy Maximization for Multiple Instance Learning based Whole Slide Image ClassificationCode2
Diffusion Models Beat GANs on Image SynthesisCode2
DiffusionLight: Light Probes for Free by Painting a Chrome BallCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
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