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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 461470 of 9051 papers

TitleStatusHype
Active Teacher for Semi-Supervised Object DetectionCode1
Bootstrapping Referring Multi-Object TrackingCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
Building a Conversational Agent Overnight with Dialogue Self-PlayCode1
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
Diversity-Guided Multi-Objective Bayesian Optimization With Batch EvaluationsCode1
A Large-Scale Database for Graph Representation LearningCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelsCode1
Diverse Image Generation via Self-Conditioned GANsCode1
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