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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 461470 of 9051 papers

TitleStatusHype
Active Teacher for Semi-Supervised Object DetectionCode1
Diverse Image Generation via Self-Conditioned GANsCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
Diverse Text Generation via Variational Encoder-Decoder Models with Gaussian Process PriorsCode1
Diverse Weight Averaging for Out-of-Distribution GeneralizationCode1
Diversified Adversarial Attacks based on Conjugate Gradient MethodCode1
A Large-Scale Database for Graph Representation LearningCode1
A Large-Scale Study on Video Action Dataset CondensationCode1
A Large-scale Universal Evaluation Benchmark For Face Forgery DetectionCode1
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill DiversificationCode1
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