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[P] Implementing Randomly Wired Neural Networks for Image Recognition, Experiments were performed on CIFAR-10 datasets and CIFAR-100 datasets.

Hi, I’m Myeongjun Kim. I am a graduate student in computer vision research. I realized the importance of paper implementation. So I implemented this paper. I wrote the code with my friend Taehun Kim, I used pytorch, and Taehun Kim wrote the code with tensorflow. Others were experimenting with ImageNet datasets. Therefore, the experiment was carried out using CIFAR datasets. There is no experiment on CIFAR datasets in the paper, but we implemented the network by putting hyper-parameters similar to the paper. There are a lot of deficiencies in the paper implementation for the first time. we are little nervous because it’s the first time we post. But we ask for a lot of feedbacks. Thank you so much for reading the long paragraph.

CIFAR-10, Accuracy: 92.65%

CIFAR-100, Accuracy: 72.92%

Pytorch version Github URL,

Tensorflow version Github URL,

submitted by /u/leaderj1001
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Toronto AI is a social and collaborative hub to unite AI innovators of Toronto and surrounding areas. We explore AI technologies in digital art and music, healthcare, marketing, fintech, vr, robotics and more. Toronto AI was founded by Dave MacDonald and Patrick O'Mara.