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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 8751–8775 of 9051 papers

TitleStatusHype
Domain Generalization for 6D Pose Estimation Through NeRF-based Image Synthesis—0
Domain Generalization for Mammographic Image Analysis with Contrastive Learning—0
Competition Dynamics in the Meme Ecosystem—0
Domain Generalization with Adversarial Intensity Attack for Medical Image Segmentation—0
Domain Generalization with MixStyle—0
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation—0
Domain-invariant Feature Exploration for Domain Generalization—0
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Comparison of various image fusion methods for impervious surface classification from VNREDSat-1—0
Domain Randomization via Entropy Maximization—0
Comparison of machine learning models applied on anonymized data with different techniques—0
Comparison of classifiers in challenge scheme—0
Domain-Specific Improvement on Psychotherapy Chatbot Using Assistant—0
Reliable and diverse evaluation of LLM medical knowledge mastery—0
Comparison and Benchmarking of AI Models and Frameworks on Mobile Devices—0
Domain-wise Adversarial Training for Out-of-Distribution Generalization—0
Addressing Data Heterogeneity in Federated Learning of Cox Proportional Hazards Models—0
Do Neural Network Cross-Modal Mappings Really Bridge Modalities?—0
Don't Bet on Luck Alone: Enhancing Behavioral Reproducibility of Quality-Diversity Solutions in Uncertain Domains—0
Don’t Burst Blindly: For a Better Use of Natural Language Processing to Fight Opinion Bubbles in News Recommendations—0
Comparing reliability of grid-based Quality-Diversity algorithms using artificial landscapes—0
Don't Look into the Dark: Latent Codes for Pluralistic Image Inpainting—0
Don't Throw Away Data: Better Sequence Knowledge Distillation—0
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets—0
VoxArabica: A Robust Dialect-Aware Arabic Speech Recognition System—0
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