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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 4101–4125 of 9051 papers

TitleStatusHype
Class Incremental Learning with Multi-Teacher DistillationCode0
Efficient Scene Recovery Using Luminous Flux Prior—0
Training Diffusion Models Towards Diverse Image Generation with Reinforcement Learning—0
Brush2Prompt: Contextual Prompt Generator for Object Inpainting—0
NightCC: Nighttime Color Constancy via Adaptive Channel Masking—0
Learning Coupled Dictionaries from Unpaired Data for Image Super-Resolution—0
Motion Diversification Networks—0
A Versatile Framework for Continual Test-Time Domain Adaptation: Balancing Discriminability and Generalizability—0
Multiplayer Battle Game-Inspired Optimizer for Complex Optimization Problems—0
Overcome Modal Bias in Multi-modal Federated Learning via Balanced Modality SelectionCode0
GAN-GA: A Generative Model based on Genetic Algorithm for Medical Image GenerationCode0
Diffusion Model with Perceptual Loss—0
Informative Rays Selection for Few-Shot Neural Radiance Fields—0
greylock: A Python Package for Measuring The Composition of Complex DatasetsCode0
Hybrid quantum cycle generative adversarial network for small molecule generation—0
FedSDD: Scalable and Diversity-enhanced Distillation for Model Aggregation in Federated Learning—0
Replica Tree-based Federated Learning using Limited DataCode0
Reinforcement-based Display-size Selection for Frugal Satellite Image Change Detection—0
T cell receptor binding prediction: A machine learning revolution—0
A proposed new metric for the conceptual diversity of a text—0
RDGCL: Reaction-Diffusion Graph Contrastive Learning for Recommendation—0
Generalizable Task Representation Learning for Offline Meta-Reinforcement Learning with Data LimitationsCode0
Towards Robust Multimodal Prompting With Missing Modalities—0
BAL: Balancing Diversity and Novelty for Active LearningCode0
Mesure simultanée des réponses impulsionnelles en macrodiversité—0
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