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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 10911100 of 9051 papers

TitleStatusHype
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal TransferCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
Curriculum-guided Hindsight Experience ReplayCode1
Content-aware Tile Generation using Exterior Boundary InpaintingCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text GenerationCode1
Contextual Diversity for Active LearningCode1
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