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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 13111320 of 9051 papers

TitleStatusHype
Contrastive Syn-to-Real GeneralizationCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Bootstrapping Referring Multi-Object TrackingCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
CelebA-Spoof: Large-Scale Face Anti-Spoofing Dataset with Rich AnnotationsCode1
Illuminating Diverse Neural Cellular Automata for Level GenerationCode1
An Informative Tracking BenchmarkCode1
Image Disentanglement Autoencoder for Steganography Without EmbeddingCode1
Imagine by Reasoning: A Reasoning-Based Implicit Semantic Data Augmentation for Long-Tailed ClassificationCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
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