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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 951975 of 9051 papers

TitleStatusHype
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
Mirror: A Multiple-perspective Self-Reflection Method for Knowledge-rich ReasoningCode1
MitoEM Dataset: Large-scale 3D Mitochondria Instance Segmentation from EM ImagesCode1
Attributed Graph Clustering with Dual Redundancy ReductionCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
F2GAN: Fusing-and-Filling GAN for Few-shot Image GenerationCode1
MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal AnglesCode1
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillationCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Domain-Smoothing Network for Zero-Shot Sketch-Based Image RetrievalCode1
Diversity-Measurable Anomaly DetectionCode1
Modeling Dynamic Topics in Chain-Free Fashion by Evolution-Tracking Contrastive Learning and Unassociated Word ExclusionCode1
Modeling Thousands of Human Annotators for Generalizable Text-to-Image Person Re-identificationCode1
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge DistillationCode1
MOFDiff: Coarse-grained Diffusion for Metal-Organic Framework DesignCode1
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading BooksCode1
Exploring Semantic Consistency and Style Diversity for Domain Generalized Semantic SegmentationCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
Monte Carlo Policy Gradient Method for Binary OptimizationCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
DLCR: A Generative Data Expansion Framework via Diffusion for Clothes-Changing Person Re-IDCode1
Aligning Language Models with Preferences through f-divergence MinimizationCode1
DLow: Diversifying Latent Flows for Diverse Human Motion PredictionCode1
Exploring Empty Spaces: Human-in-the-Loop Data AugmentationCode1
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