TLC convert the global operation to a local one so that it extract representations based on local spatial region of features as in training phase.

Although significant effort has been applied to fact-checking, the prevalence of fake news over social media, which has profound impact on justice, public trust and our society, remains a serious problem. In this work, we focus on propagation-based fake news detection, as recent studies have demonstrated that fake news and real news spread differently online. Specifically, considering the capability of graph neural networks (GNNs) in dealing with non-Euclidean data, we use GNNs to differentiate between the propagation patterns of fake and real news on social media. In particular, we concentrate on two questions: (1) Without relying on any text information, e.g., tweet content, replies and user descriptions, how accurately can GNNs identify fake news? Machine learning models are known to be vulnerable to adversarial attacks, and avoiding the dependence on text-based features can make the model less susceptible to the manipulation of advanced fake news fabricators. (2) How to deal with new, unseen data? In other words, how does a GNN trained on a given dataset perform on a new and potentially vastly different dataset? If it achieves unsatisfactory performance, how do we solve the problem without re-training the model on the entire data from scratch? We study the above questions on two datasets with thousands of labelled news items, and our results show that: (1) GNNs can achieve comparable or superior performance without any text information to state-of-the-art methods. (2) GNNs trained on a given dataset may perform poorly on new, unseen data, and direct incremental training cannot solve the problem---this issue has not been addressed in the previous work that applies GNNs for fake news detection. In order to solve the problem, we propose a method that achieves balanced performance on both existing and new datasets, by using techniques from continual learning to train GNNs incrementally.

GNNCL

Graph Neural Networks with Continual Learning for Fake News Detection from Social Media

Improving Image Restoration by Revisiting Global Information Aggregation

**Weight Modulation** is an alternative to [adaptive instance normalization](https://paperswithcode.com/method/adaptive-instance-normalization) for use in generative adversarial networks, specifically it is introduced in [StyleGAN2](https://paperswithcode.com/method/stylegan2). The purpose of [instance normalization](https://paperswithcode.com/method/instance-normalization) is to remove the effect of $s$ - the scales of the features maps - from the statistics of the [convolution](https://paperswithcode.com/method/convolution)’s output feature maps. Weight modulation tries to achieve this goal more directly. Assuming that input activations are i.i.d. random variables with unit standard deviation. After modulation and convolution, the output activations have standard deviation of:

$$ \sigma\_{j} = \sqrt{{\sum\_{i,k}w\_{ijk}'}^{2}} $$

i.e., the outputs are scaled by the $L\_{2}$ norm of the corresponding weights. The subsequent normalization aims to restore the outputs back to unit standard deviation. This can be achieved if we scale (“demodulate”) each output feature map $j$ by $1/\sigma\_{j}$ . Alternatively, we can again bake this into the convolution weights:

$$ w''\_{ijk} = w'\_{ijk} / \sqrt{{\sum\_{i, k}w'\_{ijk}}^{2} + \epsilon} $$

where $\epsilon$ is a small constant to avoid numerical issues.

Source	Improving Image Restoration by Revisiting Global Information Aggregation
Year	2000
Data Source	CC BY-SA - https://paperswithcode.com

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What is: Test-time Local Converter?

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