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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 851–900 of 9051 papers

TitleStatusHype
Global Adaptation meets Local Generalization: Unsupervised Domain Adaptation for 3D Human Pose EstimationCode1
G-Eval: NLG Evaluation using GPT-4 with Better Human AlignmentCode1
PosterLayout: A New Benchmark and Approach for Content-aware Visual-Textual Presentation LayoutCode1
KPEval: Towards Fine-Grained Semantic-Based Keyphrase EvaluationCode1
Image Quality-aware Diagnosis via Meta-knowledge Co-embeddingCode1
VisDA 2022 Challenge: Domain Adaptation for Industrial Waste SortingCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
Towards Diverse and Coherent Augmentation for Time-Series ForecastingCode1
TAPS3D: Text-Guided 3D Textured Shape Generation from Pseudo SupervisionCode1
Take 5: Interpretable Image Classification with a Handful of FeaturesCode1
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
CoDEPS: Online Continual Learning for Depth Estimation and Panoptic SegmentationCode1
Active Teacher for Semi-Supervised Object DetectionCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
Diversity-Aware Meta Visual PromptingCode1
RMMDet: Road-Side Multitype and Multigroup Sensor Detection System for Autonomous DrivingCode1
RiDDLE: Reversible and Diversified De-identification with Latent EncryptorCode1
Semi-Federated Learning for Collaborative Intelligence in Massive IoT NetworksCode1
Diversity-Measurable Anomaly DetectionCode1
Patched Diffusion Models for Unsupervised Anomaly Detection in Brain MRICode1
SemEval-2023 Task 10: Explainable Detection of Online SexismCode1
SynthASpoof: Developing Face Presentation Attack Detection Based on Privacy-friendly Synthetic DataCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Kartezio: Evolutionary Design of Explainable Pipelines for Biomedical Image AnalysisCode1
DREAM: Efficient Dataset Distillation by Representative MatchingCode1
Neural Video Compression with Diverse ContextsCode1
Key-Exchange Convolutional Auto-Encoder for Data Augmentation in Early Knee Osteoarthritis DetectionCode1
Tailoring Language Generation Models under Total Variation DistanceCode1
Diverse Policy Optimization for Structured Action SpaceCode1
AfriSenti: A Twitter Sentiment Analysis Benchmark for African LanguagesCode1
Aligning Language Models with Preferences through f-divergence MinimizationCode1
NL2CMD: An Updated Workflow for Natural Language to Bash Commands TranslationCode1
Making Substitute Models More Bayesian Can Enhance Transferability of Adversarial ExamplesCode1
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using SamplesCode1
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
Towards Geospatial Foundation Models via Continual PretrainingCode1
Mask Conditional Synthetic Satellite ImageryCode1
MMPD: Multi-Domain Mobile Video Physiology DatasetCode1
Sample-efficient Multi-objective Molecular Optimization with GFlowNetsCode1
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
LoFT: Enhancing Faithfulness and Diversity for Table-to-Text Generation via Logic Form ControlCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
ANTM: An Aligned Neural Topic Model for Exploring Evolving TopicsCode1
Evolving Flying Machines in Minecraft Using Quality DiversityCode1
ProtoSeg: Interpretable Semantic Segmentation with Prototypical PartsCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Open-World Multi-Task Control Through Goal-Aware Representation Learning and Adaptive Horizon PredictionCode1
Active learning for medical image segmentation with stochastic batchesCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
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