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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 3331–3340 of 9051 papers

TitleStatusHype
ZeroNVS: Zero-Shot 360-Degree View Synthesis from a Single ImageCode2
Towards a Unified Conversational Recommendation System: Multi-task Learning via Contextualized Knowledge DistillationCode0
Semantic Generative Augmentations for Few-Shot CountingCode1
CodeFusion: A Pre-trained Diffusion Model for Code Generation—0
MIM-GAN-based Anomaly Detection for Multivariate Time Series DataCode0
CADS: Unleashing the Diversity of Diffusion Models through Condition-Annealed Sampling—0
Blind Image Super-resolution with Rich Texture-Aware Codebooks—0
Generative Fractional Diffusion ModelsCode1
Dialect Adaptation and Data Augmentation for Low-Resource ASR: TalTech Systems for the MADASR 2023 Challenge—0
Graph Convolutional Networks for Complex Traffic Scenario Classification—0
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