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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 801–825 of 9051 papers

TitleStatusHype
TransferTOD: A Generalizable Chinese Multi-Domain Task-Oriented Dialogue System with Transfer CapabilitiesCode1
D2 Pruning: Message Passing for Balancing Diversity and Difficulty in Data PruningCode1
dacl10k: Benchmark for Semantic Bridge Damage SegmentationCode1
BenthicNet: A global compilation of seafloor images for deep learning applicationsCode1
DALDA: Data Augmentation Leveraging Diffusion Model and LLM with Adaptive Guidance ScalingCode1
Are Large Language Models Capable of Generating Human-Level Narratives?Code1
2D medical image synthesis using transformer-based denoising diffusion probabilistic modelCode1
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
Dance with You: The Diversity Controllable Dancer Generation via Diffusion ModelsCode1
Dan: Deep attention neural network for news recommendationCode1
Ego-Exo: Transferring Visual Representations from Third-person to First-person VideosCode1
DARG: Dynamic Evaluation of Large Language Models via Adaptive Reasoning GraphCode1
Entropy Minimization vs. Diversity Maximization for Domain AdaptationCode1
DART: Articulated Hand Model with Diverse Accessories and Rich TexturesCode1
Action detection using a neural network elucidates the genetics of mouse grooming behaviorCode1
Data Augmentation via Latent Diffusion for Saliency PredictionCode1
A Review on Self-Supervised Learning for Time Series Anomaly Detection: Recent Advances and Open ChallengesCode1
A Bayesian Flow Network Framework for Chemistry TasksCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Dataset Factorization for CondensationCode1
Inversion Circle Interpolation: Diffusion-based Image Augmentation for Data-scarce ClassificationCode1
Improving Diversity with Adversarially Learned Transformations for Domain GeneralizationCode1
Efficient Facial Feature Learning with Wide Ensemble-based Convolutional Neural NetworksCode1
AutoSTR: Efficient Backbone Search for Scene Text RecognitionCode1
Effect of latent space distribution on the segmentation of images with multiple annotationsCode1
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