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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 351–375 of 9051 papers

TitleStatusHype
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient SpaceCode1
Active learning for medical image segmentation with stochastic batchesCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Dan: Deep attention neural network for news recommendationCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
AbGPT: De Novo Antibody Design via Generative Language ModelingCode1
Curriculum-guided Hindsight Experience ReplayCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Active Learning by Acquiring Contrastive ExamplesCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
Cross-Image Region Mining with Region Prototypical Network for Weakly Supervised SegmentationCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
Active Domain Adaptation via Clustering Uncertainty-weighted EmbeddingsCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
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