On this basis, in order to further improve the quality of CS image reconstruction, we propose fused features and perceptual loss encoder-decoder residual network (FFPL-EDRNet) for image reconstruction ...

We propose a method for anomaly localization in industrial images using Transformer Encoder-Decoder Mask Reconstruction. The self-attention mechanism of the Transformer enables better attention to ...

The model is trained on a sequence of flattened image features and uses positional encoding to capture the spatial relationships between pixels. The encoder and decoder models are implemented with ...

Our Hamlyn researchers proposed a new AI unsupervised deep learning framework for image reconstruction ... A novel generative densely connected encoder-decoder architecture has been designed which ...

The network consists of two main parts: an encoder and a decoder. Encoder: The encoder takes the input ... They are particularly useful when dealing with high-dimensional data, such as images, where ...

A significant challenge in this field is the reconstruction ... encoder. Our model outperforms other neural networks, demonstrating the great potential of a brain-inspired model to solve the challenge ...

In recent years, single-view 3D reconstruction has emerged as a popular research topic in the AI community ... by the inclusion of an encoder-decoder structure in their framework. In this structure, ...

Then, the decoder uses the information of the encoder and generates an output, such as a translation of the input sentence in another language or a summary of a document. As Sebastian Raschka, ML and ...

The research builds upon an earlier system Meta created that can decode speech from MEG signals. The new AI system they created is made up of three parts, namely an image encoder, a brain encoder ...