Our encoder encompasses a new recurrent method to adjust graph convolution parameters without relying on node embeddings on the temporal dimension. Additionally, it incorporates generative modeling by ...

In this paper, the stacked convolutional autoencoder network structure constructed with fusion selection kernel attention mechanism is based on FCAE, which consists of an encoder and a decoder.

Recent research sheds light on the strengths and weaknesses of encoder-decoder and decoder-only models architectures in machine translation tasks.

Ranasinghe et al. (2020) proposed a convolutional autoencoder, where the encoder performs three convolutional operations, flatten and dense operations; the last dense layer is set to equal the number ...

In this letter, we propose a window transformer convolutional autoencoder (WiTCAE) to address the sparse unmixing problem. In our method, a well-designed transformer encoder for hyperspectral images ...

In this paper, through the experimental comparison of multi-layer perceptron, convolutional neural network, and the proposed convolutional autoencoder, we find that the constructed convolutional ...

Yet what is an autoencoder exactly? Briefly, autoencoders operate by taking in data, compressing and encoding the data, and then reconstructing the data from the encoding representation. The model is ...