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.

**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.

Discriminative Adversarial Search

Discriminative Adversarial Search for Abstractive Summarization

GhostNet

GhostNet: More Features from Cheap Operations

**Mobile Video Network**, or **MoViNet**, is a type of computation and memory efficient video network that can operate on streaming video for online inference. Three techniques are used to improve efficiency while reducing the peak memory usage of 3D CNNs. First, a video network search space is designed and [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) employed to generate efficient and diverse 3D CNN architectures. Second, a Stream Buffer technique is introduced that decouples memory from video clip duration, allowing 3D CNNs to embed arbitrary-length streaming video sequences for both training and inference with a small constant memory footprint. Third, a simple ensembling technique is used to improve accuracy further without sacrificing efficiency.

Source	GhostNet: More Features from Cheap Operations
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com