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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 2576–2600 of 9051 papers

TitleStatusHype
On Domain-Specific Post-Training for Multimodal Large Language Models—0
DreamBlend: Advancing Personalized Fine-tuning of Text-to-Image Diffusion Models—0
λ: A Benchmark for Data-Efficiency in Long-Horizon Indoor Mobile Manipulation Robotics—0
Enhancing weed detection performance by means of GenAI-based image augmentation—0
Diffusion Autoencoders for Few-shot Image Generation in Hyperbolic Space—0
Differential learning kinetics govern the transition from memorization to generalization during in-context learning—0
Spatiotemporal Skip Guidance for Enhanced Video Diffusion Sampling—0
Metric-DST: Mitigating Selection Bias Through Diversity-Guided Semi-Supervised Metric LearningCode0
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation—0
Network Inversion and Its Applications—0
Dual-Representation Interaction Driven Image Quality Assessment with Restoration AssistanceCode0
MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content CreationCode0
Multimodal Alignment and Fusion: A Survey—0
DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model—0
Multi-Label Bayesian Active Learning with Inter-Label RelationshipsCode0
Maximally Separated Active Learning—0
Towards Foundation Models for Critical Care Time Series—0
Multi-Resolution Generative Modeling of Human Motion from Limited Data—0
Mixed Degradation Image Restoration via Local Dynamic Optimization and Conditional Embedding—0
Understanding trade-offs in classifier bias with quality-diversity optimization: an application to talent management—0
Comparing Photorealism in Game Engines for Synthetic Maritime Computer Vision Datasets—0
PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation—0
Text-to-Image Synthesis: A Decade Survey—0
VisualLens: Personalization through Visual History—0
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