ResNet-based Convolutional Decoder-Encoder

A ResNet-based Convolutional Decoder-Encoder is a type of neural network architecture that combines the principles of Residual Networks (ResNets) and Decoder-Encoder networks. ResNets are deep neural networks that use skip connections to avoid the vanishing gradient problem and allow for the training of very deep networks. Decoder-Encoder networks, on the other hand, are used for tasks such as image segmentation, object detection, and image generation.

The ResNet-based Convolutional Decoder-Encoder architecture consists of a series of encoder layers that downsample the input image and a series of decoder layers that upsample the encoded features to generate the output image. The encoder layers typically consist of Convolutional Layers followed by Batch Normalization and ReLU activation. The decoder layers consist of transposed convolutional layers, also known as deconvolutional layers, followed by Batch Normalization and ReLU activation.

The skip connections in the ResNet-based Convolutional Decoder-Encoder architecture allow for the direct transfer of information from the encoder to the decoder layers, which helps to preserve important features and reduce the effects of information loss during the downsampling process. The resulting network can be trained end-to-end using backpropagation to minimize a loss function that measures the difference between the predicted and ground truth images.

ResNet-based Convolutional Decoder-Encoder networks have been used successfully for a variety of image reconstruction and generation tasks, including image denoising, super-resolution, and inpainting.