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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 4791–4800 of 9051 papers

TitleStatusHype
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided DiffusionCode1
TaskMix: Data Augmentation for Meta-Learning of Spoken Intent Understanding—0
Knowledge Distillation to Ensemble Global and Interpretable Prototype-Based Mammogram Classification Models—0
Self-supervised Image Clustering from Multiple Incomplete Views via Constrastive Complementary Generation—0
Open-Ended Diverse Solution Discovery with Regulated Behavior Patterns for Cross-Domain Adaptation—0
Multiple-Choice Question Generation: Towards an Automated Assessment Framework—0
Enhancing Data Diversity for Self-training Based Unsupervised Cross-modality Vestibular Schwannoma and Cochlea Segmentation—0
Semantically Consistent Data Augmentation for Neural Machine Translation via Conditional Masked Language ModelCode0
Selecting Better Samples from Pre-trained LLMs: A Case Study on Question Generation—0
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
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