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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

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Showing 1001–1025 of 9051 papers

TitleStatusHype
NEVIS'22: A Stream of 100 Tasks Sampled from 30 Years of Computer Vision ResearchCode1
NL2CMD: An Updated Workflow for Natural Language to Bash Commands TranslationCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
NoisyTwins: Class-Consistent and Diverse Image Generation through StyleGANsCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Not Just Object, But State: Compositional Incremental Learning without ForgettingCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
AlphaFold Distillation for Protein DesignCode1
O2O-Afford: Annotation-Free Large-Scale Object-Object Affordance LearningCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelsCode1
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
AlphaGarden: Learning to Autonomously Tend a Polyculture GardenCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
One2Set: Generating Diverse Keyphrases as a SetCode1
Generating Novel Scene Compositions from Single Images and VideosCode1
Online Continual Learning on a Contaminated Data Stream with Blurry Task BoundariesCode1
Online Damage Recovery for Physical Robots with Hierarchical Quality-DiversityCode1
On Pretraining Data Diversity for Self-Supervised LearningCode1
On the Affinity, Rationality, and Diversity of Hierarchical Topic ModelingCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contextual Diversity for Active LearningCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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