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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

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Showing 3621–3630 of 9051 papers

TitleStatusHype
Boosting Semantic Segmentation from the Perspective of Explicit Class EmbeddingsCode0
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
Augmenting medical image classifiers with synthetic data from latent diffusion models—0
Audio Generation with Multiple Conditional Diffusion Model—0
MolGrapher: Graph-based Visual Recognition of Chemical StructuresCode1
Evaluation of Faithfulness Using the Longest Supported Subsequence—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images—0
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Diverse Policies Converge in Reward-free Markov Decision ProcesseCode0
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