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 421430 of 9051 papers

TitleStatusHype
Cross-Utterance Conditioned VAE for Non-Autoregressive Text-to-SpeechCode1
COVID-Net CT-2: Enhanced Deep Neural Networks for Detection of COVID-19 from Chest CT Images Through Bigger, More Diverse LearningCode1
Active Finetuning: Exploiting Annotation Budget in the Pretraining-Finetuning ParadigmCode1
Synth-Empathy: Towards High-Quality Synthetic Empathy DataCode1
Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient SpaceCode1
Agree to Disagree: Diversity through Disagreement for Better TransferabilityCode1
COVIDx CT-3: A Large-scale, Multinational, Open-Source Benchmark Dataset for Computer-aided COVID-19 Screening from Chest CT ImagesCode1
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion SegmentationCode1
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading BooksCode1
Aligning Latent and Image Spaces to Connect the UnconnectableCode1
Show:102550
← PrevPage 43 of 906Next →

No leaderboard results yet.