Description: This project implements a transformer autoencoder to reconstruct images from the CIFAR-10 dataset. The model is trained on a sequence of flattened image features and uses positional ...

Acquiring a substantial amount of high-quality data for industrial image detection poses significant challenges in the field of computer vision. The imbalance between normal and anomalous samples, ...

Decoder: The decoder takes the compressed representation produced by the encoder and attempts to reconstruct the original input data from it. It essentially performs the inverse operation of the ...

Traditionally, models for single-view object reconstruction built on convolutional neural networks have shown remarkable performance in reconstruction tasks. In recent years, single-view 3D ...

Inspired by the progress in single image restoration (denoising) using deep learning, the authors propose a deep learning based approach which leverages an encoder-decoder architecture and recurrent ...

Our Hamlyn researchers proposed a new AI unsupervised deep learning framework for image reconstruction, aiming to assist minimally invasive surgery.

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

Researchers introduce ViTok, a Vision Transformer-based auto-encoder that scales visual tokenization to enhance image and video generation while reducing computational costs.