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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

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Showing 4661–4670 of 9051 papers

TitleStatusHype
Track2Vec: fairness music recommendation with a GPU-free customizable-driven frameworkCode1
Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis—0
Latent Space is Feature Space: Regularization Term for GANs Training on Limited DatasetCode0
Few-shot Image Generation via Masked DiscriminationCode0
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data—0
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?Code1
Is Out-of-Distribution Detection Learnable?—0
Uncertainty Sentence Sampling by Virtual Adversarial Perturbation—0
Full-band General Audio Synthesis with Score-based Diffusion—0
DiffusionDB: A Large-scale Prompt Gallery Dataset for Text-to-Image Generative ModelsCode3
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