SOTAVerified

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 531540 of 9051 papers

TitleStatusHype
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion ModelCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
Differentiable Quality DiversityCode1
Barbie: Text to Barbie-Style 3D AvatarsCode1
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language BenchmarkCode1
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language ModelCode1
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