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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 14411450 of 9051 papers

TitleStatusHype
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language BenchmarkCode1
Ego-Exo: Transferring Visual Representations from Third-person to First-person VideosCode1
AnthroNet: Conditional Generation of Humans via AnthropometricsCode1
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
Improving Adversarial Robustness via Promoting Ensemble DiversityCode1
Efficient Neural Neighborhood Search for Pickup and Delivery ProblemsCode1
Causal-Guided Active Learning for Debiasing Large Language ModelsCode1
Ego2Hands: A Dataset for Egocentric Two-hand Segmentation and DetectionCode1
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor ScenesCode1
HuManiFlow: Ancestor-Conditioned Normalising Flows on SO(3) Manifolds for Human Pose and Shape Distribution EstimationCode1
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