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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 176200 of 9051 papers

TitleStatusHype
Ambiguous Medical Image Segmentation using Diffusion ModelsCode2
DiverGen: Improving Instance Segmentation by Learning Wider Data Distribution with More Diverse Generative DataCode2
FLatten Transformer: Vision Transformer using Focused Linear AttentionCode2
Diffusion Models Beat GANs on Image SynthesisCode2
DiffusionLight: Light Probes for Free by Painting a Chrome BallCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
From Exploration to Mastery: Enabling LLMs to Master Tools via Self-Driven InteractionsCode2
Test-time Alignment of Diffusion Models without Reward Over-optimizationCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
Diverse Preference OptimizationCode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
Grasp-Anything: Large-scale Grasp Dataset from Foundation ModelsCode2
GroundingSuite: Measuring Complex Multi-Granular Pixel GroundingCode2
Guide to k-mer approaches for genomics across the tree of lifeCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
DialogStudio: Towards Richest and Most Diverse Unified Dataset Collection for Conversational AICode2
Dialectal Coverage And Generalization in Arabic Speech RecognitionCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
Diffusion Bridge Implicit ModelsCode2
#InsTag: Instruction Tagging for Analyzing Supervised Fine-tuning of Large Language ModelsCode2
DeTPP: Leveraging Object Detection for Robust Long-Horizon Event PredictionCode2
InstructBLIP: Towards General-purpose Vision-Language Models with Instruction TuningCode2
Is One GPU Enough? Pushing Image Generation at Higher-Resolutions with Foundation ModelsCode2
Dereflection Any Image with Diffusion Priors and Diversified DataCode2
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