The autoencoder (AE) is a fundamental deep learning approach to anomaly detection. AEs are trained on the assumption that abnormal inputs will produce higher reconstruction errors than normal ones. In ...

Self-Supervised Pre-Training with Contrastive and Masked Autoencoder Methods for Dealing with Small Datasets in Deep Learning for Medical Imaging Publication about self-supervised pre-training in ...

Tech colossus, Microsoft, has released its AI software for developers everywhere, and it can even be run from a single laptop. Open-source deep learning software has the potential of opening the ...

Artificial Intelligence (AI) pioneer Nvidia has announced it will train 100,000 developers in "deep learning" to bolster health care research and improve treatment in diseases like cancer. Deep ...

Since the SRCNN [1] model was first proposed, it has become the current research focus in this field that training deep-learning based model to perform super resolution. The current flow of ...

Current transformer saturation is a key issue for power systems because it negatively affects the operation of relays, resulting in the malfunction of protection devices. Recently, deep learning has ...