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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 15261550 of 9051 papers

TitleStatusHype
Semantic-Aligned Adversarial Evolution Triangle for High-Transferability Vision-Language AttackCode1
CodeInstruct: Empowering Language Models to Edit CodeCode1
Fair Federated Learning under Domain Skew with Local Consistency and Domain DiversityCode1
SemEval-2023 Task 10: Explainable Detection of Online SexismCode1
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic FacesCode1
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
Semi-Supervised Few-Shot Atomic Action RecognitionCode1
FairDiverse: A Comprehensive Toolkit for Fair and Diverse Information Retrieval AlgorithmsCode1
InsetGAN for Full-Body Image GenerationCode1
Automatically Generating Numerous Context-Driven SFT Data for LLMs across Diverse GranularityCode1
Class-Aware Mask-Guided Feature Refinement for Scene Text RecognitionCode1
IndoNLU: Benchmark and Resources for Evaluating Indonesian Natural Language UnderstandingCode1
Class-Balancing Diffusion ModelsCode1
Clotho: An Audio Captioning DatasetCode1
A Map of Diverse Synthetic Stable Roommates InstancesCode1
CloudEval-YAML: A Practical Benchmark for Cloud Configuration GenerationCode1
FastGrasp: Efficient Grasp Synthesis with DiffusionCode1
Inducing High Energy-Latency of Large Vision-Language Models with Verbose ImagesCode1
Automated segmentation and morphological characterization of placental histology images based on a single labeled imageCode1
Feature Fusion from Head to Tail for Long-Tailed Visual RecognitionCode1
CLoG: Benchmarking Continual Learning of Image Generation ModelsCode1
Sieve: Multimodal Dataset Pruning Using Image Captioning ModelsCode1
Continual Object Detection via Prototypical Task Correlation Guided Gating MechanismCode1
CLIP-VG: Self-paced Curriculum Adapting of CLIP for Visual GroundingCode1
Adaptive Contrastive Search: Uncertainty-Guided Decoding for Open-Ended Text GenerationCode1
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