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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 931940 of 9051 papers

TitleStatusHype
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
LTL2Action: Generalizing LTL Instructions for Multi-Task RLCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
M3ED: Multi-modal Multi-scene Multi-label Emotional Dialogue DatabaseCode1
A Large-Scale Database for Graph Representation LearningCode1
Make It Move: Controllable Image-to-Video Generation with Text DescriptionsCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image RestorationCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Contextual Diversity for Active LearningCode1
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