The model is trained until the loss is minimized and the data is reproduced as closely as possible. Through this process, an autoencoder can learn the important features of the data. While that’s a ...

LSTM autoencoder is an encoder that makes use of LSTM encoder-decoder architecture to compress data using an encoder and decode it to retain original structure using a decoder. by Ankit Das Simple ...

The objective of a contractive autoencoder is to have a robust learned representation which is less sensitive to small variation in the data. Robustness of the representation for the data is done by ...

Reference implementation for a variational autoencoder in TensorFlow and PyTorch. I recommend the PyTorch version. It includes an example of a more expressive variational family, the inverse ...

Autoencoder is a widely used deep learning method, which first extracts features from all data through unsupervised reconstruction, and then fine-tunes the network with labeled data. However, due to ...

In this paper, a secure robust JPEG steganographic scheme based on an autoencoder with an adaptive BCH encoding (Bose-Chaudhuri-Hocquenghem encoding) is proposed. In particular, the autoencoder is ...