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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 1001–1025 of 9051 papers

TitleStatusHype
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose EstimationCode1
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANsCode1
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial NetworksCode1
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text GenerationCode1
Frequency Domain Model Augmentation for Adversarial AttackCode1
DGPO: Discovering Multiple Strategies with Diversity-Guided Policy OptimizationCode1
Multimodal Multi-objective Optimization: Comparative Study of the State-of-the-ArtCode1
Interaction Pattern Disentangling for Multi-Agent Reinforcement LearningCode1
Accelerating Score-based Generative Models with Preconditioned Diffusion SamplingCode1
PGMG: A Pharmacophore-Guided Deep Learning Approach for Bioactive Molecular GenerationCode1
ReLER@ZJU-Alibaba Submission to the Ego4D Natural Language Queries Challenge 2022Code1
Forecasting Future World Events with Neural NetworksCode1
Summarizing Videos using Concentrated Attention and Considering the Uniqueness and Diversity of the Video FramesCode1
Siamese Contrastive Embedding Network for Compositional Zero-Shot LearningCode1
Dynamic-Group-Aware Networks for Multi-Agent Trajectory Prediction with Relational ReasoningCode1
VLCap: Vision-Language with Contrastive Learning for Coherent Video Paragraph CaptioningCode1
Diversified Adversarial Attacks based on Conjugate Gradient MethodCode1
Rarity Score : A New Metric to Evaluate the Uncommonness of Synthesized ImagesCode1
Personalized Federated Learning via Variational Bayesian InferenceCode1
Improving Diversity with Adversarially Learned Transformations for Domain GeneralizationCode1
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