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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 35313540 of 9051 papers

TitleStatusHype
Interactions and migration rescuing ecological diversity0
PanoMixSwap Panorama Mixing via Structural Swapping for Indoor Scene Understanding0
MVP: Meta Visual Prompt Tuning for Few-Shot Remote Sensing Image Scene Classification0
RenderIH: A Large-scale Synthetic Dataset for 3D Interacting Hand Pose EstimationCode2
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
D3: Data Diversity Design for Systematic Generalization in Visual Question AnsweringCode0
Current and future directions in network biology0
Explaining Search Result Stances to Opinionated People0
Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixerCode0
TII-SSRC-23 Dataset: Typological Exploration of Diverse Traffic Patterns for Intrusion Detection0
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