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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 49514960 of 9051 papers

TitleStatusHype
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning0
Learning Object Placement via Dual-path Graph CompletionCode1
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness0
Few-shot Image Generation Using Discrete Content Representation0
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly DetectionCode1
BigIssue: A Realistic Bug Localization Benchmark0
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
Omni3D: A Large Benchmark and Model for 3D Object Detection in the WildCode2
Pretraining a Neural Network before Knowing Its ArchitectureCode2
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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