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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 5351–5375 of 9051 papers

TitleStatusHype
Unsupervised vocal dereverberation with diffusion-based generative models—0
Pushing the limits of self-supervised speaker verification using regularized distillation framework—0
Uncertainty Quantification for Atlas-Level Cell Type Transfer—0
RITA: Boost Driving Simulators with Realistic Interactive Traffic Flow—0
Few-shot Image Generation with Diffusion ModelsCode0
Using Set Covering to Generate Databases for Holistic SteganalysisCode0
SizeGAN: Improving Size Representation in Clothing Catalogs—0
A review of TinyML—0
Diversity-based Deep Reinforcement Learning Towards Multidimensional Difficulty for Fighting Game AICode0
Rethinking the transfer learning for FCN based polyp segmentation in colonoscopyCode0
Benchmarking Quality-Diversity Algorithms on Neuroevolution for Reinforcement Learning—0
Contrastive Learning for Diverse Disentangled Foreground Generation—0
A General Purpose Neural Architecture for Geospatial Systems—0
Discussion of Features for Acoustic Anomaly Detection under Industrial Disturbing Noise in an End-of-Line Test of Geared Motors—0
An Information-Theoretic Approach for Estimating Scenario Generalization in Crowd Motion Prediction—0
Exploiting Spatial-temporal Correlations for Video Anomaly Detection—0
CLIP-Sculptor: Zero-Shot Generation of High-Fidelity and Diverse Shapes from Natural Language—0
Improving Response Diversity through Commonsense-Aware Empathetic Response Generation—0
Learning utterance-level representations through token-level acoustic latents prediction for Expressive Speech Synthesis—0
How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning?Code0
Evaluation of large-scale synthetic data for Grammar Error Correction—0
Towards Attribute-Entangled Controllable Text Generation: A Pilot Study of Blessing GenerationCode0
Period VITS: Variational Inference with Explicit Pitch Modeling for End-to-end Emotional Speech Synthesis—0
Latent Space is Feature Space: Regularization Term for GANs Training on Limited DatasetCode0
ScoreMix: A Scalable Augmentation Strategy for Training GANs with Limited Data—0
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