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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 641–650 of 9051 papers

TitleStatusHype
WoVoGen: World Volume-aware Diffusion for Controllable Multi-camera Driving Scene GenerationCode1
Diversified in-domain synthesis with efficient fine-tuning for few-shot classificationCode1
Think Twice Before Selection: Federated Evidential Active Learning for Medical Image Analysis with Domain ShiftsCode1
Generalization by Adaptation: Diffusion-Based Domain Extension for Domain-Generalized Semantic SegmentationCode1
Dynamic Inertial Poser (DynaIP): Part-Based Motion Dynamics Learning for Enhanced Human Pose Estimation with Sparse Inertial SensorsCode1
Toward Improving Robustness of Object Detectors Against Domain ShiftCode1
W-HMR: Monocular Human Mesh Recovery in World Space with Weak-Supervised CalibrationCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Diffusion ModelCode1
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion SegmentationCode1
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