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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 421430 of 9051 papers

TitleStatusHype
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
UAVDB: Trajectory-Guided Adaptable Bounding Boxes for UAV DetectionCode1
Prototype-Driven Multi-Feature Generation for Visible-Infrared Person Re-identificationCode1
HUMOS: Human Motion Model Conditioned on Body ShapeCode1
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality DataCode1
Planning In Natural Language Improves LLM Search For Code GenerationCode1
Rethinking HTG Evaluation: Bridging Generation and RecognitionCode1
Rethinking Image Super-Resolution from Training Data PerspectivesCode1
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
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