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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 4576–4600 of 9051 papers

TitleStatusHype
Boosting Semantic Segmentation from the Perspective of Explicit Class EmbeddingsCode0
Predator-prey survival pressure is sufficient to evolve swarming behaviors—0
Augmenting medical image classifiers with synthetic data from latent diffusion models—0
Efficient Transfer Learning in Diffusion Models via Adversarial Noise—0
CHORUS: Learning Canonicalized 3D Human-Object Spatial Relations from Unbounded Synthesized Images—0
Diverse Policies Converge in Reward-free Markov Decision ProcesseCode0
Progressive Feature Mining and External Knowledge-Assisted Text-Pedestrian Image Retrieval—0
Advancements in Point Cloud Data Augmentation for Deep Learning: A Survey—0
Audio Generation with Multiple Conditional Diffusion Model—0
Evaluation of Faithfulness Using the Longest Supported Subsequence—0
Generalising sequence models for epigenome predictions with tissue and assay embeddings—0
The YOLO model that still excels in document layout analysis—0
Diversity Measures: Domain-Independent Proxies for Failure in Language Model QueriesCode0
DPAN: Dynamic Preference-based and Attribute-aware Network for Relevant RecommendationsCode0
Self-Feedback DETR for Temporal Action Detection—0
Learning Clothing and Pose Invariant 3D Shape Representation for Long-Term Person Re-Identification—0
Comparing Measures of Linguistic Diversity Across Social Media Language Data and Census Data at Subnational Geographic Areas—0
DynED: Dynamic Ensemble Diversification in Data Stream ClassificationCode0
Improving Diversity in Zero-Shot GAN Adaptation with Semantic Variations—0
Adaptive pruning-based Newton's method for distributed learning—0
UAV 3-D path planning based on MOEA/D with adaptive areal weight adjustment—0
SSMG: Spatial-Semantic Map Guided Diffusion Model for Free-form Layout-to-Image Generation—0
UniAP: Towards Universal Animal Perception in Vision via Few-shot Learning—0
Robust Fraud Detection via Supervised Contrastive Learning—0
DUAW: Data-free Universal Adversarial Watermark against Stable Diffusion Customization—0
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