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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 7326–7350 of 9051 papers

TitleStatusHype
Distinguishing Cell Phenotype Using Cell EpigenotypeCode0
The generalized Price equation: forces that change population statistics—0
Local Implicit Grid Representations for 3D ScenesCode0
Diversity, Density, and Homogeneity: Quantitative Characteristic Metrics for Text Collections—0
GIQA: Generated Image Quality AssessmentCode1
Federated Visual Classification with Real-World Data DistributionCode0
DLow: Diversifying Latent Flows for Diverse Human Motion PredictionCode1
Boosting Unconstrained Face Recognition with Auxiliary Unlabeled Data—0
The Engagement-Diversity Connection: Evidence from a Field Experiment on Spotify—0
Cytology Image Analysis Techniques Towards Automation: Systematically Revisited—0
Multimodal Shape Completion via Conditional Generative Adversarial NetworksCode1
PTP: Parallelized Tracking and Prediction with Graph Neural Networks and Diversity Sampling—0
Pool-Based Unsupervised Active Learning for Regression Using Iterative Representativeness-Diversity Maximization (iRDM)—0
ActiLabel: A Combinatorial Transfer Learning Framework for Activity Recognition—0
Many-Objective Estimation of Distribution Optimization Algorithm Based on WGAN-GP—0
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout GenerationCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
Diversity can be Transferred: Output Diversification for White- and Black-box AttacksCode1
GMM-UNIT: Unsupervised Multi-Domain and Multi-Modal Image-to-Image Translation via Attribute Gaussian Mixture ModelingCode0
Imperialist Competitive Algorithm with Independence and Constrained Assimilation for Solving 0-1 Multidimensional Knapsack Problem—0
Learn to Augment: Joint Data Augmentation and Network Optimization for Text RecognitionCode1
AutoSTR: Efficient Backbone Search for Scene Text RecognitionCode1
Evaluating Logical Generalization in Graph Neural NetworksCode1
Interactive Neural Style Transfer with ArtistsCode0
TAFSSL: Task-Adaptive Feature Sub-Space Learning for few-shot classificationCode1
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