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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 5351–5375 of 9051 papers

TitleStatusHype
Learning Enriched Illuminants for Cross and Single Sensor Color Constancy—0
RareGAN: Generating Samples for Rare ClassesCode0
Recognising the importance of preference change: A call for a coordinated multidisciplinary research effort in the age of AI—0
Learning Affordance Grounding from Exocentric ImagesCode1
Challenges and Strategies in Cross-Cultural NLP—0
On the Importance of Data Size in Probing Fine-tuned ModelsCode0
MotionAug: Augmentation with Physical Correction for Human Motion PredictionCode1
Multilingual Detection of Personal Employment Status on TwitterCode0
Semantic-diversity transfer network for generalized zero-shot learning via inner disagreement based OOD detector—0
Ask to Understand: Question Generation for Multi-hop Question Answering—0
PMAL: Open Set Recognition via Robust Prototype Mining—0
Structurally Diverse Sampling for Sample-Efficient Training and Comprehensive EvaluationCode0
On Redundancy and Diversity in Cell-based Neural Architecture SearchCode0
Towards Afrocentric NLP for African Languages: Where We Are and Where We Can Go—0
Self-Supervised Deep Learning to Enhance Breast Cancer Detection on Screening Mammography—0
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
Fantastic Style Channels and Where to Find Them: A Submodular Framework for Discovering Diverse Directions in GANs—0
Complex Evolutional Pattern Learning for Temporal Knowledge Graph ReasoningCode1
SUPERB-SG: Enhanced Speech processing Universal PERformance Benchmark for Semantic and Generative Capabilities—0
Diversifying Content Generation for Commonsense Reasoning with Mixture of Knowledge Graph ExpertsCode1
InsetGAN for Full-Body Image GenerationCode1
The Principle of Diversity: Training Stronger Vision Transformers Calls for Reducing All Levels of RedundancyCode1
Peng Cheng Object Detection Benchmark for Smart City—0
Towards Analyzing the Bias of News Recommender Systems Using Sentiment and Stance Detection—0
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label EnhancementCode1
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