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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 6851–6875 of 9051 papers

TitleStatusHype
User Fairness, Item Fairness, and Diversity for Rankings in Two-Sided Markets—0
Deep Incomplete Multi-View Multiple Clusterings—0
Online Knowledge Distillation via Multi-branch Diversity Enhancement—0
MEGATRON-CNTRL: Controllable Story Generation with External Knowledge Using Large-Scale Language Models—0
MGD-GAN: Text-to-Pedestrian generation through Multi-Grained Discrimination—0
Plan-CVAE: A Planning-based Conditional Variational Autoencoder for Story Generation—0
Deep Color Transfer using Histogram AnalogyCode1
ENTYFI: A System for Fine-grained Entity Typing in Fictional Texts—0
A new dataset of dog breed images and a benchmark for fine-grained classification—0
A Niching Indicator-Based Multi-modal Many-objective Optimizer—0
Utilizing Transfer Learning and a Customized Loss Function for Optic Disc Segmentation from Retinal Images—0
Towards automatic visual inspection: A weakly supervised learning method for industrial applicable object detection—0
Ask-n-Learn: Active Learning via Reliable Gradient Representations for Image Classification—0
A Framework to Handle Multi-modal Multi-objective Optimization in Decomposition-based Evolutionary Algorithms—0
DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of EnsemblesCode1
Multiple Word Embeddings for Increased Diversity of RepresentationCode0
CrowdMOT: Crowdsourcing Strategies for Tracking Multiple Objects in Videos—0
Data Instance Prior for Transfer Learning in GANs—0
Discriminative Representation Loss (DRL): A More Efficient Approach than Gradient Re-Projection in Continual Learning—0
The Bures Metric for Taming Mode Collapse in Generative Adversarial Networks—0
Cuid: A new study of perceived image quality and its subjective assessment—0
Mitigating Gender Bias for Neural Dialogue Generation with Adversarial LearningCode1
EvolGAN: Evolutionary Generative Adversarial NetworksCode1
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach—0
Controllable Text Generation with Focused Variation—0
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