The spurious correlations over hybrid distribution deviation degrade the performance of previous GNN methods and show large instability among different datasets.
Instead of replacing and retraining the fully connected neural network classifer, Geometer predicts the label of a node by finding the nearest class prototype.
To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities.
In recent years, image recognition applications have developed rapidly.
We propose a lightweight CPU network based on the MKLDNN acceleration strategy, named PP-LCNet, which improves the performance of lightweight models on multiple tasks.
Optical Character Recognition (OCR) systems have been widely used in various of application scenarios.