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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 13011310 of 9051 papers

TitleStatusHype
DiffAgent: Fast and Accurate Text-to-Image API Selection with Large Language ModelCode1
Memory Sharing for Large Language Model based AgentsCode1
Differentiable Quality DiversityCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
GIQA: Generated Image Quality AssessmentCode1
GenView: Enhancing View Quality with Pretrained Generative Model for Self-Supervised LearningCode1
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