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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 241250 of 9051 papers

TitleStatusHype
3DGen: Triplane Latent Diffusion for Textured Mesh GenerationCode2
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy PerceptionCode2
Streaming Active Learning with Deep Neural NetworksCode2
A Vector Quantized Approach for Text to Speech Synthesis on Real-World Spontaneous SpeechCode2
Effective Data Augmentation With Diffusion ModelsCode2
Diffusion Probabilistic Models beat GANs on Medical ImagesCode2
Learning Video Representations from Large Language ModelsCode2
Scaling Language-Image Pre-training via MaskingCode2
Person Image Synthesis via Denoising Diffusion ModelCode2
SinDiffusion: Learning a Diffusion Model from a Single Natural ImageCode2
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