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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 1111–1120 of 9051 papers

TitleStatusHype
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models—0
Global Tensor Motion PlanningCode1
Differential learning kinetics govern the transition from memorization to generalization during in-context learning—0
Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Diffusion Autoencoders for Few-shot Image Generation in Hyperbolic Space—0
Spectral-Spatial Transformer with Active Transfer Learning for Hyperspectral Image ClassificationCode1
GAPartManip: A Large-scale Part-centric Dataset for Material-Agnostic Articulated Object ManipulationCode5
Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric LearningCode0
Multi-Label Bayesian Active Learning with Inter-Label RelationshipsCode0
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