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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 11611170 of 9051 papers

TitleStatusHype
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout GenerationCode1
How Consistent are Clinicians? Evaluating the Predictability of Sepsis Disease Progression with Dynamics ModelsCode1
CompOFA: Compound Once-For-All Networks for Faster Multi-Platform DeploymentCode1
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative ModelsCode1
From Sky to the Ground: A Large-scale Benchmark and Simple Baseline Towards Real Rain RemovalCode1
Rethinking conditional GAN training: An approach using geometrically structured latent manifoldsCode1
Adding Seemingly Uninformative Labels Helps in Low Data RegimesCode1
GenDexGrasp: Generalizable Dexterous GraspingCode1
Comprehensive Image Captioning via Scene Graph DecompositionCode1
Fractal Autoencoders for Feature SelectionCode1
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