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 ...

This repository is a Tensorflow implementation of the paper "Autoencoder based image compression: can the learning be quantization independent?", ICASSP, 2018. cd ...

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 ...

Sparse autoencoders are central tools in analyzing how large language models function internally. Translating complex internal states into interpretable components allows researchers to break down ...