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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 47314740 of 9051 papers

TitleStatusHype
Habitat-Matterport 3D Semantics Dataset0
The quest for the definition of life0
CORE: A Retrieve-then-Edit Framework for Counterfactual Data GenerationCode0
EVA3D: Compositional 3D Human Generation from 2D Image CollectionsCode2
Semi-supervised Semantic Segmentation with Prototype-based Consistency RegularizationCode1
SCAM! Transferring humans between images with Semantic Cross Attention ModulationCode1
Efficient Learning of Locomotion Skills through the Discovery of Diverse Environmental Trajectory Generator Priors0
A Self-attention Guided Multi-scale Gradient GAN for Diversified X-ray Image Synthesis0
Take a Fresh Look at Recommender Systems from an Evaluation Standpoint0
SDA: Simple Discrete Augmentation for Contrastive Sentence Representation LearningCode0
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