**Seq2Seq**, or **Sequence To Sequence**, is a model used in sequence prediction tasks, such as language modelling and machine translation. The idea is to use one [LSTM](https://paperswithcode.com/method/lstm), the *encoder*, to read the input sequence one timestep at a time, to obtain a large fixed dimensional vector representation (a context vector), and then to use another LSTM, the *decoder*, to extract the output sequence
from that vector. The second LSTM is essentially a recurrent neural network language model except that it is conditioned on the input sequence.

(Note that this page refers to the original seq2seq not general sequence-to-sequence models)

A **Multiscale Dilated Convolution Block** is an Inception-style convolutional block motivated by the ideas that image features naturally occur at multiple scales, that a network’s expressivity is proportional to the range of functions it can represent divided by its total number of parameters, and by the desire to efficiently expand a network’s receptive field. The Multiscale [Dilated Convolution](https://paperswithcode.com/method/dilated-convolution) (MDC) block applies a single $F\times{F}$ filter at multiple dilation factors, then performs a weighted elementwise sum of each dilated filter’s output, allowing the network to simultaneously learn a set of features and the relevant scales at which those features occur with a minimal increase in parameters. This also rapidly expands the network’s receptive field without requiring an increase in depth or the number of parameters.

Multiscale Dilated Convolution Block

Neural Photo Editing with Introspective Adversarial Networks

Seq2Seq

Sequence to Sequence Learning with Neural Networks

Inspired on the widely known [spatial squeeze and channel excitation (SE)](https://paperswithcode.com/method/squeeze-and-excitation-block) block, the sSE block performs channel squeeze and spatial excitation, to recalibrate the feature maps spatially and achieve more fine-grained image segmentation.

Source	Sequence to Sequence Learning with Neural Networks
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com