SOTAVerified

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 47114720 of 9051 papers

TitleStatusHype
Learning Diversified Feature Representations for Facial Expression Recognition in the WildCode0
Packed-Ensembles for Efficient Uncertainty Estimation0
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
ReasonChainQA: Text-based Complex Question Answering with Explainable Evidence Chains0
Improving Contrastive Learning on Visually Homogeneous Mars Rover Images0
A Patch-Based Algorithm for Diverse and High Fidelity Single Image GenerationCode0
ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational ModelCode1
Style Transfer as Data Augmentation: A Case Study on Named Entity RecognitionCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
LEATHER: A Framework for Learning to Generate Human-like Text in DialogueCode0
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