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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1551–1600 of 9051 papers

TitleStatusHype
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
Cross-Covariate Gait Recognition: A BenchmarkCode1
Recommendations for Item Set Completion: On the Semantics of Item Co-Occurrence With Data Sparsity, Input Size, and Input ModalitiesCode1
ReDAL: Region-based and Diversity-aware Active Learning for Point Cloud Semantic SegmentationCode1
A Quantum Leaky Integrate-and-Fire Spiking Neuron and NetworkCode1
Refiner: Refining Self-attention for Vision TransformersCode1
Reinforcement learning on structure-conditioned categorical diffusion for protein inverse foldingCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
CRoSS: Diffusion Model Makes Controllable, Robust and Secure Image SteganographyCode1
Remastering Divide and Remaster: A Cinematic Audio Source Separation Dataset with Multilingual SupportCode1
CreoPep: A Universal Deep Learning Framework for Target-Specific Peptide Design and OptimizationCode1
Interpretable Unsupervised Diversity Denoising and Artefact RemovalCode1
Clotho: An Audio Captioning DatasetCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
ReSmooth: Detecting and Utilizing OOD Samples when Training with Data AugmentationCode1
Restart Sampling for Improving Generative ProcessesCode1
Cross-Domain Feature Augmentation for Domain GeneralizationCode1
Rethinking Data Augmentation for Single-source Domain Generalization in Medical Image SegmentationCode1
Curriculum-guided Hindsight Experience ReplayCode1
Re-thinking Federated Active Learning based on Inter-class DiversityCode1
Rethinking Guidance Information to Utilize Unlabeled Samples:A Label Encoding PerspectiveCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
RetroPrime: A Diverse, plausible and Transformer-based method for Single-Step retrosynthesis predictionsCode1
Revealing the Dark Secrets of Masked Image ModelingCode1
ReViT: Enhancing Vision Transformers Feature Diversity with Attention Residual ConnectionsCode1
Rewarded soups: towards Pareto-optimal alignment by interpolating weights fine-tuned on diverse rewardsCode1
CMoralEval: A Moral Evaluation Benchmark for Chinese Large Language ModelsCode1
RIGNO: A Graph-based framework for robust and accurate operator learning for PDEs on arbitrary domainsCode1
AARGH! End-to-end Retrieval-Generation for Task-Oriented DialogCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Robustness of Graph Neural Networks at ScaleCode1
RoPGen: Towards Robust Code Authorship Attribution via Automatic Coding Style TransformationCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
RuBLiMP: Russian Benchmark of Linguistic Minimal PairsCode1
RU-Net: Regularized Unrolling Network for Scene Graph GenerationCode1
COAST: COntrollable Arbitrary-Sampling NeTwork for Compressive SensingCode1
EmpHi: Generating Empathetic Responses with Human-like IntentsCode1
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
AcroFOD: An Adaptive Method for Cross-domain Few-shot Object DetectionCode1
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
Accelerating AI and Computer Vision for Satellite Pose Estimation on the Intel Myriad X Embedded SoC—0
Automated Circuit Sizing with Multi-objective Optimization based on Differential Evolution and Bayesian Inference—0
Multiscale guidance of AlphaFold3 with heterogeneous cryo-EM data—0
Automated Backend-Aware Post-Training Quantization—0
Automated Adversarial Discovery for Safety Classifiers—0
Always Strengthen Your Strengths: A Drift-Aware Incremental Learning Framework for CTR Prediction—0
Show:102550
← PrevPage 32 of 182Next →

No leaderboard results yet.