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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 12211230 of 9051 papers

TitleStatusHype
Beyond Performance Plateaus: A Comprehensive Study on Scalability in Speech EnhancementCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Generating Smooth Pose Sequences for Diverse Human Motion PredictionCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
KPEval: Towards Fine-Grained Semantic-Based Keyphrase EvaluationCode1
KQA Pro: A Dataset with Explicit Compositional Programs for Complex Question Answering over Knowledge BaseCode1
Generate, Prune, Select: A Pipeline for Counterspeech Generation against Online Hate SpeechCode1
General Virtual Sketching Framework for Vector Line ArtCode1
Generating Diverse 3D Reconstructions from a Single Occluded Face ImageCode1
Generalized Probabilistic U-Net for medical image segementationCode1
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