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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

Showing 526550 of 9051 papers

TitleStatusHype
Distributed speech separation in spatially unconstrained microphone arraysCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
DIG In: Evaluating Disparities in Image Generations with Indicators for Geographic DiversityCode1
Adversarial Semantic Data Augmentation for Human Pose EstimationCode1
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
DirectMultiStep: Direct Route Generation for Multi-Step RetrosynthesisCode1
Distribution-aware Knowledge Prototyping for Non-exemplar Lifelong Person Re-identificationCode1
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline GenerationCode1
Adversarial Parametric Pose PriorCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
Diffusion Reward: Learning Rewards via Conditional Video DiffusionCode1
Diffusion for Out-of-Distribution Detection on Road Scenes and BeyondCode1
DiffuseExpand: Expanding dataset for 2D medical image segmentation using diffusion modelsCode1
COM Kitchens: An Unedited Overhead-view Video Dataset as a Vision-Language BenchmarkCode1
DiffuSum: Generation Enhanced Extractive Summarization with DiffusionCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
Batched Bayesian optimization by maximizing the probability of including the optimumCode1
DiffSketching: Sketch Control Image Synthesis with Diffusion ModelsCode1
3D Vision and Language Pretraining with Large-Scale Synthetic DataCode1
Diff-Mosaic: Augmenting Realistic Representations in Infrared Small Target Detection via Diffusion PriorCode1
DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion ModelsCode1
Differential Evolution with Reversible Linear TransformationsCode1
Differentiable Quality DiversityCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
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