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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
DVG-Face: Dual Variational Generation for Heterogeneous Face RecognitionCode1
Dyadic Interaction Modeling for Social Behavior GenerationCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial SensorsCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Ferret: Faster and Effective Automated Red Teaming with Reward-Based Scoring TechniqueCode1
Biological Sequence Design with GFlowNetsCode1
BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face GenerationCode1
AlphaFold Distillation for Protein DesignCode1
Few-shot Defect Image Generation based on Consistency ModelingCode1
Few-Shot Video Object DetectionCode1
FLAIR: a Country-Scale Land Cover Semantic Segmentation Dataset From Multi-Source Optical ImageryCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
Generative Category-Level Shape and Pose Estimation with Semantic PrimitivesCode1
AlphaGarden: Learning to Autonomously Tend a Polyculture GardenCode1
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
Effect of latent space distribution on the segmentation of images with multiple annotationsCode1
Online Continual Learning on a Contaminated Data Stream with Blurry Task BoundariesCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
EEV: A Large-Scale Dataset for Studying Evoked Expressions from VideoCode1
Effective Diversity in Population Based Reinforcement LearningCode1
On the Affinity, Rationality, and Diversity of Hierarchical Topic ModelingCode1
FastGrasp: Efficient Grasp Synthesis with DiffusionCode1
Fast Batch Nuclear-norm Maximization and Minimization for Robust Domain AdaptationCode1
Bias Loss for Mobile Neural NetworksCode1
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