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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 911–920 of 9051 papers

TitleStatusHype
Contextual Diversity for Active LearningCode1
Learning Semantic-Aligned Feature Representation for Text-based Person SearchCode1
Learning Texture Invariant Representation for Domain Adaptation of Semantic SegmentationCode1
Learning to Generate Novel Domains for Domain GeneralizationCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled DataCode1
Towards Task Sampler Learning for Meta-LearningCode1
Learning to See by Looking at NoiseCode1
Content-aware Tile Generation using Exterior Boundary InpaintingCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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