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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 521530 of 9051 papers

TitleStatusHype
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Adversarial Semantic Data Augmentation for Human Pose EstimationCode1
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
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