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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 14911500 of 9051 papers

TitleStatusHype
Open-World Instance Segmentation: Exploiting Pseudo Ground Truth From Learned Pairwise AffinityCode1
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon PredictionCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
Optimal Counterfactual Explanations for Scorecard modellingCode1
Controllable Group Choreography using Contrastive DiffusionCode1
CompOFA: Compound Once-For-All Networks for Faster Multi-Platform DeploymentCode1
Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19Code1
Optimizing Readability Using Genetic AlgorithmsCode1
Adaptive Diffusion Terrain Generator for Autonomous Uneven Terrain NavigationCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
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