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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 281290 of 9051 papers

TitleStatusHype
Deep Rectangling for Image Stitching: A Learning BaselineCode2
DeepPrivacy2: Towards Realistic Full-Body AnonymizationCode2
AdaSociety: An Adaptive Environment with Social Structures for Multi-Agent Decision-MakingCode2
Delta Decompression for MoE-based LLMs CompressionCode2
Curvature Diversity-Driven Deformation and Domain Alignment for Point CloudCode2
EVA3D: Compositional 3D Human Generation from 2D Image CollectionsCode2
DAD-3DHeads: A Large-scale Dense, Accurate and Diverse Dataset for 3D Head Alignment from a Single ImageCode2
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
DEP-RL: Embodied Exploration for Reinforcement Learning in Overactuated and Musculoskeletal SystemsCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
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