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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 8401–8425 of 9051 papers

TitleStatusHype
Synthetic data augmentation for robotic mobility aids to support blind and low vision people—0
Distilling Privileged Multimodal Information for Expression Recognition using Optimal Transport—0
Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification—0
Distributed Antenna Selection for Massive MIMO using Reversing Petri Nets—0
Distributed Deep Learning for Modulation Classification in 6G Cell-Free Wireless Networks—0
Synthetic Data Generation for Augmenting Small Samples—0
Distributed Maximization of Submodular plus Diversity Functions for Multi-label Feature Selection on Huge Datasets—0
Distributed Multi-Head Learning Systems for Power Consumption Prediction—0
Conditional Single-view Shape Generation for Multi-view Stereo Reconstruction—0
Advancing Cross-Organ Domain Generalization with Test-Time Style Transfer and Diversity Enhancement—0
Distribution Aligned Multimodal and Multi-Domain Image Stylization—0
Distribution augmentation for low-resource expressive text-to-speech—0
Conditional Neural Generation using Sub-Aspect Functions for Extractive News Summarization—0
Distribution Aware Metrics for Conditional Natural Language Generation—0
Synthetic Data Generation for Residential Load Patterns via Recurrent GAN and Ensemble Method—0
Distribution Learning Based on Evolutionary Algorithm Assisted Deep Neural Networks for Imbalanced Image Classification—0
Distribution-restrained Softmax Loss for the Model Robustness—0
DiTAR: Diffusion Transformer Autoregressive Modeling for Speech Generation—0
How Diversely Can Language Models Solve Problems? Exploring the Algorithmic Diversity of Model-Generated Code—0
DITTO-NeRF: Diffusion-based Iterative Text To Omni-directional 3D Model—0
div2vec: Diversity-Emphasized Node Embedding—0
Conditional Distribution Modelling for Few-Shot Image Synthesis with Diffusion Models—0
DivAvatar: Diverse 3D Avatar Generation with a Single Prompt—0
DivBO: Diversity-aware CASH for Ensemble Learning—0
CONDEN-FI: Consistency and Diversity Learning-based Multi-View Unsupervised Feature and In-stance Co-Selection—0
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