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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 42414250 of 9051 papers

TitleStatusHype
Leaping Into Memories: Space-Time Deep Feature SynthesisCode0
More Robust Schema-Guided Dialogue State Tracking via Tree-Based Paraphrase Ranking0
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
SigVIC: Spatial Importance Guided Variable-Rate Image Compression0
Taming Diffusion Models for Audio-Driven Co-Speech Gesture GenerationCode2
Exploring Resiliency to Natural Image Corruptions in Deep Learning using Design Diversity0
Active Teacher for Semi-Supervised Object DetectionCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
Redrawing attendance boundaries to promote racial and ethnic diversity in elementary schools0
Diversity-Aware Meta Visual PromptingCode1
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