The AttendSeg deep learning model performs semantic segmentation at an accuracy that is almost on-par with RefineNet while cutting down the number of parameters to 1.19 million.

The rapid development of deep learning in recent years is largely due to the rapid increase in the scale of data. The availability of large amounts of data is revolutionary for model training by the ...

Deci, a company aiming to optimize deep learning models, is releasing a new family of models for image classification. These models outperform well-known alternatives in both accuracy and runtime ...

The Global Mapper Insight and Learning Engine™ (Beta) provides trained models for land cover classification, vehicle identification, and building extraction. This update to Global Mapper ...

Many deep-learning image classification networks have already made heavy use of transfer learning, and it's likely that the design approach will continue to play an important role in pushing ...

Examples of Deep Learning: Image Recognition: Deep learning is used in image recognition systems, like facial recognition or object detection in self-driving cars, where convolutional neural ...

In traditional semiconductor packaging, manual defect review after automated optical inspection (AOI) is an arduous task for operators and engineers, involving review of both good and bad die. It is ...

The novel method was presented in “ SPF-Net: Solar panel fault detection using U-Net based deep learning image classification,” published in Energy Reports.

Deep learning architectures are used in this area based on a new convolutional neural network that can classify skin lesions with improved accuracy. Methods: A public dataset of skin lesions HAM10000 ...