SOTAVerified

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 13411350 of 9051 papers

TitleStatusHype
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Instruction-Tuning Data Synthesis from Scratch via Web ReconstructionCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
Intra-Source Style Augmentation for Improved Domain GeneralizationCode1
Inv-Entropy: A Fully Probabilistic Framework for Uncertainty Quantification in Language ModelsCode1
Inverse Materials Design by Large Language Model-Assisted Generative FrameworkCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Generating images of rare concepts using pre-trained diffusion modelsCode1
ConZIC: Controllable Zero-shot Image Captioning by Sampling-Based PolishingCode1
Show:102550
← PrevPage 135 of 906Next →

No leaderboard results yet.