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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 29913000 of 9051 papers

TitleStatusHype
GBSS:a global building semantic segmentation dataset for large-scale remote sensing building extraction0
Cheetah: Natural Language Generation for 517 African LanguagesCode0
Diversity-aware Buffer for Coping with Temporally Correlated Data Streams in Online Test-time Adaptation0
Understanding and Improving Source-free Domain Adaptation from a Theoretical Perspective0
HomoFormer: Homogenized Transformer for Image Shadow RemovalCode1
Class Incremental Learning with Multi-Teacher DistillationCode0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning0
Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster MemoryCode1
Ensemble Diversity Facilitates Adversarial TransferabilityCode1
Motion Diversification Networks0
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