A **ShuffleNet Block** is an image model block that utilises a [channel shuffle](https://paperswithcode.com/method/channel-shuffle) operation, along with depthwise convolutions, for an efficient architectural design. It was proposed as part of the [ShuffleNet](https://paperswithcode.com/method/shufflenet) architecture. The starting point is the [Residual Block](https://paperswithcode.com/method/residual-block) unit from [ResNets](https://paperswithcode.com/method/resnet), which is then modified with a pointwise group [convolution](https://paperswithcode.com/method/convolution) and a channel shuffle operation.

**PP-OCR** is an OCR system that consists of three parts, text detection, detected boxes rectification and text recognition. The purpose of text detection is to locate the text area in the image. In PP-OCR, Differentiable Binarization (DB) is used as text detector which is based on a simple segmentation network. It integrates feature extraction and sequence modeling. It adopts the Connectionist Temporal Classification (CTC) loss to avoid the inconsistency between prediction and label.

PP-OCR

PP-OCR: A Practical Ultra Lightweight OCR System

ShuffleNet Block

ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices

**node2vec** is a framework for learning graph embeddings for nodes in graphs. Node2vec maximizes a likelihood objective over mappings which preserve neighbourhood distances in higher dimensional spaces. From an algorithm design perspective, node2vec exploits the freedom to define neighbourhoods for nodes and provide an explanation for the effect of the choice of neighborhood on the learned representations. 

For each node, node2vec simulates biased random walks based on an efficient network-aware search strategy and the nodes appearing in the random walk define neighbourhoods. The search strategy accounts for the relative influence nodes exert in a network. It also generalizes prior work alluding to naive search strategies by providing flexibility in exploring neighborhoods.

Source	ShuffleNet: An Extremely Efficient Convolutional Neural Network for Mobile Devices
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com