SVG Autoencoder - Uses a frozen representation encoder with a residual branch to compensate the information loss and a learned convolutional decoder to transfer the SVG latent space to pixel space.
AZoSensors on MSN
AI maps heat inside steelmaking’s critical sintering process beds
The Temporal Fusion Transformer model provides near-real-time insights into sintering temperatures, addressing critical challenges in steelmaking processes.
A fault detection method for power conversion circuits using thermal images and a convolutional autoencoder is presented. The autoencoder is trained on thermal images captured from a commercial power ...
Abstract: We introduce a new convolutional autoencoder architecture for user modeling and recommendation tasks with several improvements over the state of the art. First, our model has the flexibility ...
Image Denoising with Autoencoders in R (University Project) Built a convolutional autoencoder in R using Keras/TensorFlow to perform image denoising on MNIST and CIFAR-10 datasets with varying levels ...
The rapid growth of unlabeled time-series data in domains such as wireless communications, radar, biomedical engineering, and the Internet of Things (IoT) has driven advancements in unsupervised ...
AI-driven robots being developed for future aged care.AIREC Waseda/YouTube In Tokyo, a humanoid robot is being tested as a potential caregiver for Japan’s aging population. Dubbed AIREC (AI-driven ...
In Tokyo, a humanoid robot is being tested as a potential caregiver for Japan's aging population. Dubbed AIREC (AI-driven Robot for Embrace and Care), the system recently demonstrated its ability to ...
Anomaly detection is a typical binary classification problem under the condition of unbalanced samples, which has been widely used in various fields of data mining. For example, it can help detect ...
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