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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 3831–3840 of 9051 papers

TitleStatusHype
EXPLOITING SEMANTIC COHERENCE TO IMPROVE PREDICTION IN SATELLITE SCENE IMAGE ANALYSIS: APPLICATION TO DISEASE DENSITY ESTIMATION—0
Exploiting Representation Bias for Data Distillation in Abstractive Text Summarization—0
Comprehensive Pathological Image Segmentation via Teacher Aggregation for Tumor Microenvironment Analysis—0
From Shadow Segmentation to Shadow Removal—0
A Sentiment-Controllable Topic-to-Essay Generator with Topic Knowledge Graph—0
AgroXAI: Explainable AI-Driven Crop Recommendation System for Agriculture 4.0—0
Exploiting Knowledge Distillation for Few-Shot Image Generation—0
Exploiting Joint Robustness to Adversarial Perturbations—0
Comprehensive Evaluation of Matrix Factorization Models for Collaborative Filtering Recommender Systems—0
Exploiting Feature Diversity for Make-up Temporal Video Grounding—0
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