To mitigate these drawbacks, we propose an Autoencoder with a Memory Module (AMM) to realize video anomaly detection ... angle discrepancies between the queries and memory items. At the training stage ...

one where all the clusters have different number of elements and another were some of the clusters have equal number of elements. The function generate_datasets from the datasets.py file returns thee ...

However, the deep SVDD model has disadvantages such as feature information loss and feature collapse during training process ... called improved autoencoder with memory module (IAEMM). Specifically, ...

This project is an unofficial implementation of MAE with the support of Beneufit, Inc.. Transformers were built purely using PyTorch and Einops library. Positional Encoding token modules were also ...

Autoencoder architecture for ADK protein ... The remaining metrics, RMSD, and DOPE, are used for the decoder module to compare the differences between the training and decoded structures. Moreover, to ...

Thus, we propose an ECG anomaly detection framework (ECG-AAE) based on an adversarial autoencoder and temporal convolutional network (TCN) which consists of three modules (autoencoder ... of simply ...