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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 4951–4975 of 9051 papers

TitleStatusHype
Exploiting Diversity of Unlabeled Data for Label-Efficient Semi-Supervised Active Learning—0
Learning Object Placement via Dual-path Graph CompletionCode1
Learning Better Registration to Learn Better Few-Shot Medical Image Segmentation: Authenticity, Diversity, and Robustness—0
Few-shot Image Generation Using Discrete Content Representation—0
Dynamic Local Aggregation Network with Adaptive Clusterer for Anomaly DetectionCode1
BigIssue: A Realistic Bug Localization Benchmark—0
DeltaGAN: Towards Diverse Few-shot Image Generation with Sample-Specific DeltaCode1
Omni3D: A Large Benchmark and Model for 3D Object Detection in the WildCode2
Pretraining a Neural Network before Knowing Its ArchitectureCode2
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
FaceFormer: Scale-aware Blind Face Restoration with Transformers—0
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
Large Scale Radio Frequency Signal ClassificationCode2
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
Unsupervised Medical Image Translation with Adversarial Diffusion ModelsCode2
GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks—0
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
On the Usefulness of Deep Ensemble Diversity for Out-of-Distribution DetectionCode0
Neural Data-to-Text Generation Based on Small Datasets: Comparing the Added Value of Two Semi-Supervised Learning Approaches on Top of a Large Language Model—0
D-CBRS: Accounting For Intra-Class Diversity in Continual Learning—0
River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index—0
Fuse It More Deeply! A Variational Transformer with Layer-Wise Latent Variable Inference for Text GenerationCode1
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