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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 10111020 of 9051 papers

TitleStatusHype
Few-shot Image Generation via Cross-domain CorrespondenceCode1
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object DetectionCode1
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption GenerationCode1
Generating Synthetic Clinical Data that Capture Class Imbalanced Distributions with Generative Adversarial Networks: Example using Antiretroviral Therapy for HIVCode1
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
FALL-E: A Foley Sound Synthesis Model and StrategiesCode1
EEG-ConvTransformer for Single-Trial EEG based Visual Stimuli ClassificationCode1
On Disentangling Spoof Trace for Generic Face Anti-SpoofingCode1
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space ShrinkingCode1
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
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