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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 171180 of 9051 papers

TitleStatusHype
Anomaly Detection via Reverse Distillation from One-Class EmbeddingCode2
EvalGIM: A Library for Evaluating Generative Image ModelsCode2
Exploring the Effect of Dataset Diversity in Self-Supervised Learning for Surgical Computer VisionCode2
Exploring the Limits of Transfer Learning with a Unified Text-to-Text TransformerCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
ASpanFormer: Detector-Free Image Matching with Adaptive Span TransformerCode2
Fields of The World: A Machine Learning Benchmark Dataset For Global Agricultural Field Boundary SegmentationCode2
A Generalizable Anomaly Detection Method in Dynamic GraphsCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
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