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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 10261050 of 9051 papers

TitleStatusHype
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
AdaptPose: Cross-Dataset Adaptation for 3D Human Pose Estimation by Learnable Motion GenerationCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Controllable Text Generation via Probability Density Estimation in the Latent SpaceCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Analysis of diversity-accuracy tradeoff in image captioningCode1
Controllable Multi-Interest Framework for RecommendationCode1
DebateQA: Evaluating Question Answering on Debatable KnowledgeCode1
Analysis and Evaluation of Synthetic Data Generation in Speech Dysfluency DetectionCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Controllable Open-ended Question Generation with A New Question Type OntologyCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Content-aware Tile Generation using Exterior Boundary InpaintingCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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