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

Object classification with basic computer vision techniques

El-Gamal on Elliptic Curve (Python)

Async API for controlling Hue Lights

Train the HRNet model on ImageNet

😊 Python module for face feature changing

Pytorch Lightning Distributed Accelerators using Ray

Exploring Versatile Prior for Human Motion via Motion Frequency Guidance (3DV2021)

Llvlir - Low Level Variable Length Intermediate Representation

Code/data of the paper "Hand-Object Contact Prediction via Motion-Based Pseudo-Labeling and Guided Progressive Label Correction" (BMVC2021)

For medical image segmentation

rliable is an open-source Python library for reliable evaluation, even with a handful of runs, on reinforcement learning and machine learnings benchmarks.

Official Pytorch implementation of "CLIPstyler:Image Style Transfer with a Single Text Condition"

Expressive Power of Invariant and Equivaraint Graph Neural Networks (ICLR 2021)

MediaPipeのPythonパッケージのサンプルです。2020/12/11時点でPython実装のある4機能(Hands、Pose、Face Mesh、Holistic)について用意しています。

An improvement of FasterGICP: Acceptance-rejection Sampling based 3D Lidar Odometry

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