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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 52215230 of 9051 papers

TitleStatusHype
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector0
Semantic Diversity versus Visual Diversity in Visual Dictionaries0
Semantic Example Guided Image-to-Image Translation0
Semantic Exploration with Adaptive Gating for Efficient Problem Solving with Language Models0
Semantic-Guided Generative Image Augmentation Method with Diffusion Models for Image Classification0
Semantic Map Guided Synthesis of Wireless Capsule Endoscopy Images using Diffusion Models0
Semantic Neighborhood Ordering in Multi-objective Genetic Programming based on Decomposition0
Semantic-Rearrangement-Based Multi-Level Alignment for Domain Generalized Segmentation0
Semantic Scene Completion with Multi-Feature Data Balancing Network0
Semantics Disentangling for Text-to-Image Generation0
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