Achieving high reliability in AI systems—such as autonomous vehicles that stay on course even in snowstorms or medical AI that can diagnose cancer from low-resolution images—depends heavily on model ...

Abstract: Data augmentation is a series of techniques that generate high-quality artificial data by manipulating existing data samples. By leveraging data augmentation techniques, AI models can ...

Abstract: Data augmentation is crucial for addressing insufficient training data, especially for augmenting positive samples. However, existing methods mostly rely on neural network-based feedback for ...

Source: ChatGPT modified by NostaLab. Let's fall into the dystopian rabbit hole and look, not avert our eyes. We like to believe that working with AI makes us better thinkers. The interaction feels ...

“Recent advances in deep learning have promoted EEG decoding for BCI systems, but data sparsity—caused by high costs of EEG collection and inter-subject variability—still limits model performance.

Muvon Therapeutics has linked its cell therapy to a 60% drop in stress incontinence episode frequency in a small phase 2 trial, leading the biotech to pitch the candidate as a way to reduce reliance ...

This repository contains training, generation and utility scripts for Stable Diffusion. To change the weight, remove wd14_tagger_model folder, and run the script again. --max_data_loader_n_workers ...

Simone Biles is opening up about her plastic surgery. After sharing earlier this month with her TikTok followers that she underwent three cosmetic enhancements, Biles posted another TikTok Nov. 19 ...

I am trying to use torchtitan with procedurally generated data (data augmentation). This process is CPU-intensive and I strongly do not want to store each sample before. Under this setup, torchtitan ...