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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 30113020 of 9051 papers

TitleStatusHype
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution0
A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability0
EMAGE: Towards Unified Holistic Co-Speech Gesture Generation via Expressive Masked Audio Gesture ModelingCode3
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality SelectionCode0
Multiplayer Battle Game-Inspired Optimizer for Complex Optimization Problems0
Improving Text Embeddings with Large Language ModelsCode3
ODAQ: Open Dataset of Audio QualityCode1
Diffusion Model with Perceptual Loss0
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image GenerationCode0
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