Abstract: Medical image segmentation plays a central role in enhancing diagnosis, surgical planning, and treatment strategies, and in this work, the focus is on segmenting retinal vessels from color ...
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.
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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