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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 12611270 of 9051 papers

TitleStatusHype
AutoMix: Automatically Mixing Language ModelsCode1
GAN-based generative modelling for dermatological applications -- comparative studyCode1
Automating Rigid Origami DesignCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
General and Task-Oriented Video SegmentationCode1
BiRT: Bio-inspired Replay in Vision Transformers for Continual LearningCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
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