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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 901–925 of 9051 papers

TitleStatusHype
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent SpaceCode1
Latent Denoising Diffusion GAN: Faster sampling, Higher image qualityCode1
Controllable Multi-Interest Framework for RecommendationCode1
Coralai: Intrinsic Evolution of Embodied Neural Cellular Automata EcosystemsCode1
Learning Affordance Grounding from Exocentric ImagesCode1
Learning Distinct and Representative Styles for Image CaptioningCode1
Learning Diverse Risk Preferences in Population-based Self-playCode1
Learning from Missing Relations: Contrastive Learning with Commonsense Knowledge Graphs for Commonsense InferenceCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Learning Object Placement via Dual-path Graph CompletionCode1
Learning Semantic Latent Directions for Accurate and Controllable Human Motion PredictionCode1
Learning Texture Invariant Representation for Domain Adaptation of Semantic SegmentationCode1
AIM-Fair: Advancing Algorithmic Fairness via Selectively Fine-Tuning Biased Models with Contextual Synthetic DataCode1
Toward a Plug-and-Play Vision-Based Grasping Module for RoboticsCode1
Learning to Regrasp by Learning to PlaceCode1
Towards Task Sampler Learning for Meta-LearningCode1
A View From Somewhere: Human-Centric Face RepresentationsCode1
Learn to Augment: Joint Data Augmentation and Network Optimization for Text RecognitionCode1
Contextual Diversity for Active LearningCode1
Less is More: Learning from Synthetic Data with Fine-grained Attributes for Person Re-IdentificationCode1
Active Teacher for Semi-Supervised Object DetectionCode1
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection BiasCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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