SOTAVerified

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 11761200 of 9051 papers

TitleStatusHype
Generalized Probabilistic U-Net for medical image segementationCode1
Can 3D Vision-Language Models Truly Understand Natural Language?Code1
BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural NetworksCode1
Contextual Diversity for Active LearningCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
GenDexGrasp: Generalizable Dexterous GraspingCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Contrastive Syn-to-Real GeneralizationCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
Improving Diversity with Adversarially Learned Transformations for Domain GeneralizationCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
C2C-GenDA: Cluster-to-Cluster Generation for Data Augmentation of Slot FillingCode1
General and Task-Oriented Video SegmentationCode1
Between Lines of Code: Unraveling the Distinct Patterns of Machine and Human ProgrammersCode1
Generating Smooth Pose Sequences for Diverse Human Motion PredictionCode1
Bootstrapping Referring Multi-Object TrackingCode1
Controlling Behavioral Diversity in Multi-Agent Reinforcement LearningCode1
IMPUS: Image Morphing with Perceptually-Uniform Sampling Using Diffusion ModelsCode1
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
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