Learn five of the best practices for training image recognition models with artificial intelligence (AI) and limited data, such as data augmentation, transfer learning, active learning, synthetic ...

We optimize the sampling stage of the diffusion model to make it more suitable for generating high-quality EEG signals, and then enhance the original EEG signals and apply them to emotion recognition ...

ResNet-34 might not be suitable for multi-task learning on this dataset, unless of course it can be tested with more data, and with no class imbalances as was present in this dataset. Given more time, ...

Recognition of assembly tasks is needed to prevent quality defects in the Seru production system. To meet this need, in this study, a skeleton-based deep learning hybrid Convolutional Neural ...

Introducing TSDS: An Optimized Approach for Data Selection. Researchers from the University of Wisconsin-Madison, Yale University, and Apple introduce TSDS (Task-Specific Data Selection), an AI ...

Retrieval-augmented generation, or RAG, integrates external data sources to reduce hallucinations and improve the response accuracy of large language models. Retrieval-augmented generation (RAG ...

Choose a data source and a problem, preprocess and label your images, train and validate your model, and test and deploy your model. Learn how to use image recognition for data mining, using ...