Official Codes for Graph Modularity:Towards Understanding the Cross-Layer Transition of Feature Representations in Deep Neural Networks.

Last update: Dec 14, 2022

Overview

Dynamic-Graphs-Construction

Official Codes for Graph Modularity:Towards Understanding the Cross-Layer Transition of Feature Representations in Deep Neural Networks.

We give an example to understand the dynamic graphs: we leverage pretrained ResNet18 (Top1 acc: 94.78%) on CIFAR-10 to extract feature representations. Then we set N = 50, k = 3 for dynamic graphs construction. The result is shown in the gif below: Note that nodes of the same color represent samples of the same class. The darker the color of the edge, the higher the similarity between the two corresponding nodes. The modularity curve of this dynamic graph:

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Official Codes for Graph Modularity:Towards Understanding the Cross-Layer Transition of Feature Representations in Deep Neural Networks.

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Overview

Dynamic-Graphs-Construction

Owner

The code for SAG-DTA: Prediction of Drug–Target Affinity Using Self-Attention Graph Network.

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An implementation of paper `Real-time Convolutional Neural Networks for Emotion and Gender Classification` with PaddlePaddle.

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