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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

Showing 12011210 of 9051 papers

TitleStatusHype
Enhance Image Classification via Inter-Class Image Mixup with Diffusion ModelCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
Ensemble Diversity Facilitates Adversarial TransferabilityCode1
ATHENA: A Framework based on Diverse Weak Defenses for Building Adversarial DefenseCode1
eProduct: A Million-Scale Visual Search Benchmark to Address Product Recognition ChallengesCode1
Beyond Boundaries: Learning a Universal Entity Taxonomy across Datasets and Languages for Open Named Entity RecognitionCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Contrastive Syn-to-Real GeneralizationCode1
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