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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 3431–3440 of 9051 papers

TitleStatusHype
BOP-Elites, a Bayesian Optimisation algorithm for Quality-Diversity search—0
Diversified Visual Attention Networks for Fine-Grained Object Classification—0
Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition—0
Exploiting Cross-Lingual Speaker and Phonetic Diversity for Unsupervised Subword Modeling—0
Diversified Texture Synthesis with Feed-forward Networks—0
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
Exploiting Joint Robustness to Adversarial Perturbations—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
A Collection of Deep Learning-based Feature-Free Approaches for Characterizing Single-Objective Continuous Fitness Landscapes—0
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