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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 251260 of 9051 papers

TitleStatusHype
EDGE: Editable Dance Generation From MusicCode2
Towards Building Text-To-Speech Systems for the Next Billion UsersCode2
DeepPrivacy2: Towards Realistic Full-Body AnonymizationCode2
A Novel Sampling Scheme for Text- and Image-Conditional Image Synthesis in Quantized Latent SpacesCode2
Learning Heterogeneous Agent Cooperation via Multiagent League TrainingCode2
LaMAR: Benchmarking Localization and Mapping for Augmented RealityCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
EVA3D: Compositional 3D Human Generation from 2D Image CollectionsCode2
Pix2Struct: Screenshot Parsing as Pretraining for Visual Language UnderstandingCode2
ASpanFormer: Detector-Free Image Matching with Adaptive Span TransformerCode2
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