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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 591600 of 9051 papers

TitleStatusHype
Diverse and Admissible Trajectory Forecasting through Multimodal Context UnderstandingCode1
Diverse and Specific Clarification Question Generation with KeywordsCode1
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
A Doubly Decoupled Network for edge detectionCode1
DivAug: Plug-in Automated Data Augmentation with Explicit Diversity MaximizationCode1
DivClust: Controlling Diversity in Deep ClusteringCode1
Distributed speech separation in spatially unconstrained microphone arraysCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
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