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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 4101–4125 of 9051 papers

TitleStatusHype
AIRIVA: A Deep Generative Model of Adaptive Immune Repertoires—0
Concept-Monitor: Understanding DNN training through individual neurons—0
Effect of latent space distribution on the segmentation of images with multiple annotationsCode1
EasyPortrait -- Face Parsing and Portrait Segmentation DatasetCode2
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelsCode1
Mixing Data Augmentation with Preserving Foreground Regions in Medical Image Segmentation—0
GlyphDiffusion: Text Generation as Image Generation—0
Flickr-PAD: New Face High-Resolution Presentation Attack Detection DatabaseCode0
Learning Trajectories are Generalization Indicators—0
Img2Vec: A Teacher of High Token-Diversity Helps Masked AutoEncoders—0
Disagreement amongst counterfactual explanations: How transparency can be deceptive—0
Benchmark tasks for Quality-Diversity applied to Uncertain domainsCode0
TIGTEC : Token Importance Guided TExt Counterfactuals—0
Towards Mode Balancing of Generative Models via Diversity WeightsCode0
Quality-Diversity Optimisation on a Physical Robot Through Dynamics-Aware and Reset-Free Learning—0
You Never Get a Second Chance To Make a Good First Impression: Seeding Active Learning for 3D Semantic SegmentationCode1
Semi-Supervised Semantic Segmentation With Region RelevanceCode0
SATIN: A Multi-Task Metadataset for Classifying Satellite Imagery using Vision-Language Models—0
Constructing a meta-learner for unsupervised anomaly detection—0
Quantifying the difference between phylogenetic diversity and diversity indices—0
E Pluribus Unum: Guidelines on Multi-Objective Evaluation of Recommender SystemsCode0
HyperTuner: A Cross-Layer Multi-Objective Hyperparameter Auto-Tuning Framework for Data Analytic ServicesCode0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
Anything-3D: Towards Single-view Anything Reconstruction in the WildCode3
Learning Representative Trajectories of Dynamical Systems via Domain-Adaptive ImitationCode0
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