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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 501550 of 9051 papers

TitleStatusHype
Explain Me the Painting: Multi-Topic Knowledgeable Art Description GenerationCode1
AlpaCare:Instruction-tuned Large Language Models for Medical ApplicationCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
Deep Color Transfer using Histogram AnalogyCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
Exploring Effective Data for Surrogate Training Towards Black-Box AttackCode1
Exploring Inter-Channel Correlation for Diversity-Preserved Knowledge DistillationCode1
Exploring Inter-Channel Correlation for Diversity-preserved KnowledgeDistillationCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery DetectionCode1
Unconstrained Face-Mask & Face-Hand Datasets: Building a Computer Vision System to Help Prevent the Transmission of COVID-19Code1
Dataset GrowthCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
Dataset Factorization for CondensationCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Adversarial Semantic Data Augmentation for Human Pose EstimationCode1
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code ContributionsCode1
Data Augmentation Approaches in Natural Language Processing: A SurveyCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Deep Sketch-Based Modeling: Tips and TricksCode1
DAG: Depth-Aware Guidance with Denoising Diffusion Probabilistic ModelsCode1
Adversarial Parametric Pose PriorCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Curiosity-Driven Reinforcement Learning from Human FeedbackCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
Curriculum-guided Hindsight Experience ReplayCode1
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language BenchmarkCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Covariance Matrix Adaptation for the Rapid Illumination of Behavior SpaceCode1
Cousins Of The Vendi Score: A Family Of Similarity-Based Diversity Metrics For Science And Machine LearningCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
An Empirical Investigation of Pre-Trained Transformer Language Models for Open-Domain Dialogue GenerationCode1
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