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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 11111120 of 9051 papers

TitleStatusHype
Deep Image Harmonization with Learnable AugmentationCode1
Deep Ordinal Regression with Label DiversityCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
DEFN: Dual-Encoder Fourier Group Harmonics Network for Three-Dimensional Indistinct-Boundary Object SegmentationCode1
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
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