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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 441450 of 9051 papers

TitleStatusHype
Attention in Diffusion Model: A Survey0
Zero-Shot 4D Lidar Panoptic Segmentation0
Crossing Boundaries: Leveraging Semantic Divergences to Explore Cultural Novelty in Cooking RecipesCode0
Rubrik's Cube: Testing a New Rubric for Evaluating Explanations on the CUBE dataset0
Beyond a Single Mode: GAN Ensembles for Diverse Medical Data GenerationCode0
MuseFace: Text-driven Face Editing via Diffusion-based Mask Generation Approach0
Intrinsically-Motivated Humans and Agents in Open-World ExplorationCode0
Beyond Academic Benchmarks: Critical Analysis and Best Practices for Visual Industrial Anomaly DetectionCode0
VideoGen-Eval: Agent-based System for Video Generation EvaluationCode3
MHTS: Multi-Hop Tree Structure Framework for Generating Difficulty-Controllable QA Datasets for RAG Evaluation0
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