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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 10711080 of 9051 papers

TitleStatusHype
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
Contextual Diversity for Active LearningCode1
A Multi-Loss Strategy for Vehicle Trajectory Prediction: Combining Off-Road, Diversity, and Directional Consistency LossesCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Content-aware Tile Generation using Exterior Boundary InpaintingCode1
Adaptively Sparse TransformersCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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