**ProxylessNet-GPU** is a convolutional neural network architecture learnt with the [ProxylessNAS](https://paperswithcode.com/method/proxylessnas) [neural architecture search](https://paperswithcode.com/method/neural-architecture-search) algorithm that is optimized for GPU devices. It uses inverted residual blocks (MBConvs) from [MobileNetV2](https://paperswithcode.com/method/mobilenetv2) as its basic building block.

**Feature Pyramid Grids**, or **FPG**, is a deep multi-pathway feature pyramid, that represents the feature scale-space as a regular grid of parallel bottom-up pathways which are fused by multi-directional lateral connections. It connects the backbone features, $C$, of a ConvNet with a regular structure of $p$ parallel top-down pyramid pathways which are fused by multi-directional lateral connections, AcrossSame, AcrossUp, AcrossDown, and AcrossSkip. AcrossSkip are direct connections while all other types use [convolutional](https://paperswithcode.com/method/convolution) and [ReLU](https://paperswithcode.com/method/relu) layers.

On a high-level, FPG is a deep generalization of [FPN](https://paperswithcode.com/method/fpn) from one to $p$ pathways under a dense lateral connectivity structure.

Feature Pyramid Grids

ProxylessNet-GPU

ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware

The **Enhanced Fusion Framework** proposes three different ideas to improve the existing MI-based BCI frameworks.

Image source: [Fumanal-Idocin et al.](https://arxiv.org/pdf/2101.06968v1.pdf)

Source	ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com