**Discriminative Adversarial Search**, or **DAS**, is a sequence decoding approach which aims to alleviate the effects of exposure bias and to optimize on the data distribution itself rather than for external metrics. Inspired by generative adversarial networks (GANs), wherein a discriminator is used to improve the generator, DAS differs from GANs in that the generator parameters are not updated at training time and the discriminator is only used to drive sequence generation at inference time.

A **ResNeXt** repeats a building block that aggregates a set of transformations with the same topology. Compared to a [ResNet](https://paperswithcode.com/method/resnet), it exposes a new dimension,  *cardinality* (the size of the set of transformations) $C$, as an essential factor in addition to the dimensions of depth and width. 

Formally, a set of aggregated transformations can be represented as: $\mathcal{F}(x)=\sum_{i=1}^{C}\mathcal{T}_i(x)$, where $\mathcal{T}_i(x)$ can be an arbitrary function. Analogous to a simple neuron, $\mathcal{T}_i$ should project $x$ into an (optionally low-dimensional) embedding and then transform it.

ResNeXt

Aggregated Residual Transformations for Deep Neural Networks

Discriminative Adversarial Search

Discriminative Adversarial Search for Abstractive Summarization

A **GhostNet** is a type of convolutional neural network that is built using Ghost modules, which aim to generate more features by using fewer parameters (allowing for greater efficiency). 

GhostNet mainly consists of a stack of Ghost bottlenecks with the Ghost modules as the building block. The first layer is a standard convolutional layer with 16 filters, then a series of Ghost bottlenecks with gradually increased channels follow. These Ghost bottlenecks are grouped into different stages according to the sizes of their input feature maps. All the Ghost bottlenecks are applied with stride=1 except that the last one in each stage is with stride=2. At last a [global average pooling](https://paperswithcode.com/method/global-average-pooling) and a convolutional layer are utilized to transform the feature maps to a 1280-dimensional feature vector for final classification. The squeeze and excite (SE) module is also applied to the residual layer in some ghost bottlenecks. 

In contrast to [MobileNetV3](https://paperswithcode.com/method/mobilenetv3), GhostNet does not use [hard-swish](https://paperswithcode.com/method/hard-swish) nonlinearity function due to its large latency.

Source	Discriminative Adversarial Search for Abstractive Summarization
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com