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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 301–350 of 9051 papers

TitleStatusHype
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
LAKE-RED: Camouflaged Images Generation by Latent Background Knowledge Retrieval-Augmented DiffusionCode2
DeepPanoContext: Panoramic 3D Scene Understanding with Holistic Scene Context Graph and Relation-based OptimizationCode1
Barcode Method for Generative Model Evaluation driven by Topological Data AnalysisCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
Adapting Precomputed Features for Efficient Graph CondensationCode1
BanglaParaphrase: A High-Quality Bangla Paraphrase DatasetCode1
Balancing Diversity and Risk in LLM Sampling: How to Select Your Method and Parameter for Open-Ended Text GenerationCode1
Deep Image Harmonization with Learnable AugmentationCode1
AdaptDiffuser: Diffusion Models as Adaptive Self-evolving PlannersCode1
AgentGen: Enhancing Planning Abilities for Large Language Model based Agent via Environment and Task GenerationCode1
Barbie: Text to Barbie-Style 3D AvatarsCode1
Deep Ordinal Regression with Label DiversityCode1
Deep Sketch-Based Modeling: Tips and TricksCode1
Bacteriophage classification for assembled contigs using Graph Convolutional NetworkCode1
Back to Reality: Weakly-supervised 3D Object Detection with Shape-guided Label EnhancementCode1
DeepFacePencil: Creating Face Images from Freehand SketchesCode1
Adaptable Agent Populations via a Generative Model of PoliciesCode1
BackdoorMBTI: A Backdoor Learning Multimodal Benchmark Tool Kit for Backdoor Defense EvaluationCode1
BalaGAN: Image Translation Between Imbalanced Domains via Cross-Modal TransferCode1
Deep generative selection models of T and B cell receptor repertoires with soNNiaCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
AdaFocus V2: End-to-End Training of Spatial Dynamic Networks for Video RecognitionCode1
Deep Diversity-Enhanced Feature Representation of Hyperspectral ImagesCode1
DeeperForensics-1.0: A Large-Scale Dataset for Real-World Face Forgery DetectionCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
Deep Time Series Forecasting with Shape and Temporal CriteriaCode1
A Case for Rejection in Low Resource ML DeploymentCode1
Decoding Matters: Addressing Amplification Bias and Homogeneity Issue for LLM-based RecommendationCode1
DATED: Guidelines for Creating Synthetic Datasets for Engineering Design ApplicationsCode1
Dataset GrowthCode1
DeCoAR 2.0: Deep Contextualized Acoustic Representations with Vector QuantizationCode1
Grounding Language to Autonomously-Acquired Skills via Goal GenerationCode1
Data Augmentation using Pre-trained Transformer ModelsCode1
Automating Rigid Origami DesignCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
AutoMix: Automatically Mixing Language ModelsCode1
Parameterized Synthetic Text Generation with SimpleStoriesCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Dataset Factorization for CondensationCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Active Teacher for Semi-Supervised Object DetectionCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
DALNet: A Rail Detection Network Based on Dynamic Anchor LineCode1
Dan: Deep attention neural network for news recommendationCode1
Data Augmentation Alone Can Improve Adversarial TrainingCode1
Automatic Data Augmentation for 3D Medical Image SegmentationCode1
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