Our uni-fied framework shows that encoder-decoder CNN architecture is closely related to nonlinear frame representation using combinatorial convolution frames, whose expressibility increases exponen-tially with the depth. We also demonstrate the importance of skipped connection in terms of ex-pressibility, and optimization landscape. 1.

Encoder-decoder architectures can handle inputs and outputs that both consist of variable-length sequences and thus are suitable for sequence-to-sequence problems such as machine translation. The encoder takes a variable-length sequence as input and transforms it into a state with a fixed shape.

Jan 12, 2024 · CNN as Encoder, RNN/LSTM as Decoder: This architecture can be used for tasks like image captioning, where the input is an image and the output is a sequence of words describing the image. The CNN can extract features from the image, while the RNN/LSTM can generate the corresponding text sequence.

Mar 10, 2021 · There are three main blocks in the encoder-decoder model, The Encoder will convert the input sequence into a single-dimensional vector (hidden vector). The decoder will convert the hidden...

Jan 22, 2019 · Inspired by recent theoretical understanding on generalizability, expressivity and optimization landscape of neural networks, as well as the theory of convolutional framelets, here we provide a unified theoretical framework that leads to a better understanding of geometry of encoder-decoder CNNs.

An Encoder-Decoder Architecture is a deep learning framework used to handle sequence-to-sequence tasks. It is primarily used in machine translation, speech recognition, ... (CNN) that processes an image and generates a latent vector, which the decoder uses to generate a descriptive caption.

Jan 22, 2019 · Inspired by recent theoretical understanding on generalizability, expressivity and optimization landscape of neural networks, as well as the theory of convolutional framelets, here we provide a unified theoretical framework that leads to a better understanding of geometry of encoder-decoder CNNs.

Feb 6, 2021 · In this work, we focus on investigating the profound influence of the CNN-based encoder-decoder model on SOD, and providing an empirical study on the performance by applying encoder-decoder models to SOD task.

Apr 6, 2023 · A Convolutional (CNN/CNN)-based Encoder-Decoder Neural Network is an encoder-decoder neural network that consists of a encoder neural network and a decoder neural network in which one or both are convolutional neural …

Our unified framework shows that encoder-decoder CNN architecture is closely related to nonlinear frame representation using combinatorial convo-lution frames, whose expressivity increases ex-ponentially with the depth. We also demonstrate the importance of skipped connection in terms of expressivity, and optimization landscape. 1. Introduction.