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.

Related tags

Overview

Dynamic-Graphs-Construction

Owner

Official Implementation of LARGE: Latent-Based Regression through GAN Semantics

Learning to Reconstruct 3D Non-Cuboid Room Layout from a Single RGB Image

Full-featured Decision Trees and Random Forests learner.

A Transformer-Based Siamese Network for Change Detection

A simple python stock Predictor

Official implementation of the paper "AAVAE: Augmentation-AugmentedVariational Autoencoders"

Evaluating Privacy-Preserving Machine Learning in Critical Infrastructures: A Case Study on Time-Series Classification

Code for Understanding Pooling in Graph Neural Networks

Model Quantization Benchmark

PartImageNet is a large, high-quality dataset with part segmentation annotations

Accepted at ICCV-2021: Workshop on Computer Vision for Automated Medical Diagnosis (CVAMD)

Implementation of NÜWA, state of the art attention network for text to video synthesis, in Pytorch

code for `Look Closer to Segment Better: Boundary Patch Refinement for Instance Segmentation`

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Official code release for ICCV 2021 paper SNARF: Differentiable Forward Skinning for Animating Non-rigid Neural Implicit Shapes.

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STARCH compuets regional extreme storm physical characteristics and moisture balance based on spatiotemporal precipitation data from reanalysis or climate model data.