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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 19511960 of 9051 papers

TitleStatusHype
Few-Shot Image Generation by Conditional Relaxing Diffusion Inversion0
A Two-stage Evolutionary Framework For Multi-objective Optimization0
Improving the Transferability of Adversarial Examples by Feature Augmentation0
Virtual Personas for Language Models via an Anthology of BackstoriesCode1
General and Task-Oriented Video SegmentationCode1
Beyond Aesthetics: Cultural Competence in Text-to-Image ModelsCode0
V-VIPE: Variational View Invariant Pose Embedding0
Spatial Focused Bitemporal Interactive Network for Remote Sensing Image Change DetectionCode0
Leveraging image captions for selective whole slide image annotationCode0
Analysis of genetic diversity among some Iraqi durum wheat cultivars revealed by different molecular markers0
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