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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 3281–3290 of 9051 papers

TitleStatusHype
SCONE-GAN: Semantic Contrastive learning-based Generative Adversarial Network for an end-to-end image translation—0
DeepPatent2: A Large-Scale Benchmarking Corpus for Technical Drawing UnderstandingCode0
Sea You Later: Metadata-Guided Long-Term Re-Identification for UAV-Based Multi-Object Tracking—0
Measuring Adversarial Datasets—0
Tailoring Self-Rationalizers with Multi-Reward DistillationCode0
SegGen: Supercharging Segmentation Models with Text2Mask and Mask2Img Synthesis—0
Dense Video Captioning: A Survey of Techniques, Datasets and Evaluation Protocols—0
Objectives Are All You Need: Solving Deceptive Problems Without Explicit Diversity Maintenance—0
LLMs grasp morality in concept—0
Perturbation-based Active Learning for Question Answering—0
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