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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 44514460 of 9051 papers

TitleStatusHype
Wasserstein Expansible Variational Autoencoder for Discriminative and Generative Continual LearningCode0
Zero-Shot Point Cloud Segmentation by Semantic-Visual Aware SynthesisCode1
Quality Diversity for Visual Pre-Training0
Progressive Spatio-Temporal Prototype Matching for Text-Video RetrievalCode1
CHORUS : Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images0
AGAIN: Adversarial Training With Attribution Span Enlargement and Hybrid Feature FusionCode0
StyleGene: Crossover and Mutation of Region-Level Facial Genes for Kinship Face SynthesisCode1
LP-DIF: Learning Local Pattern-Specific Deep Implicit Function for 3D Objects and ScenesCode0
Mask-Guided Matting in the Wild0
Hybrid Active Learning via Deep Clustering for Video Action Detection0
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