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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 831840 of 9051 papers

TitleStatusHype
Attribute Group Editing for Reliable Few-shot Image GenerationCode1
Addressing the Elephant in the Room: Robust Animal Re-Identification with Unsupervised Part-Based Feature AlignmentCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Controllable Video Captioning with an Exemplar SentenceCode1
ART: Automatic Red-teaming for Text-to-Image Models to Protect Benign UsersCode1
AgentSense: Benchmarking Social Intelligence of Language Agents through Interactive ScenariosCode1
HomoFormer: Homogenized Transformer for Image Shadow RemovalCode1
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout GenerationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Contrastive Syn-to-Real GeneralizationCode1
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