Will your CVPR/ICML paper be accepted?

Ever wondered if we could build a deep learning neural network which can predict whether your research paper will be submitted or not in the conference? If yes, then I am sure this blog will be an interesting read.

I was recommended an interesting paper a couple of months ago and the following blog will be more of a summary of the paper – https://arxiv.org/pdf/1812.08775.pdf

With the increase in the deep learning domain, we have seen a surge in the number of papers which are submitted in the conferences. Traditionally, “peer review” system was in place to select/reject the paper. However, the authors of this paper have discussed a deep convolutional network approach which can help in automating the process.

One has to keep in mind that the author of this paper – Jia-Bin Huang has taken the “gestalt” way. It basically means that he is classifying the papers just by the way they look! After reading this I am sure that you are jumping on your seats to try out a nlp based approach, I’d suggest to be patient and keep reading 🙂 .

What did they achieve?

Quoting directly  –

Trained on ICCV/CVPR conference and workshop papers from 2013 – 2017, our deep network based classifier achieves 92% accuracy on papers in CVPR 2018. Our model safely rejects the number of bad paper submissions by 50% while sacrificing only 0.4% of good paper submissions. Our system can thus be used as a pre-filter in a cascade of the paper review process.

Dataset –

The paper says that they crawled this website and gathered the conference and workshop papers from 2013-2017. They only downloaded the papers with more than 7 pages.

The pdf2image python wrapper was used to convert the papers to images. The original size of the converted image is of size 2200 × 3400 pixels. The images were then resized to 224 × 224 pixels for both training and testing.

The data preprocessing is interesting, you should read about that in paper!

Deep Neural Network Architecture –

ResNet architecture was used.


Just the last layer which has 1000 nodes in the picture(because of imageNet dataset) was replaced by 2 output nodes. (transfer learning!)

This ImageNet pre-trained network was fine tuned with stochastic gradient descent (SGD) with a momentum of 0.9 for a total of 50 epochs. The learning rate was set to 0.001 and it was decayed by a factor of 0.1 after every 10 epochs. The loss function was weighted cross-entropy loss.

Using GANS to generate good CVPR papers?

The author has used progressive growing of GANs technique to generate papers which is fun to look – https://www.youtube.com/watch?v=yQLsZLf02yg&feature=youtu.be

Limitations –

The paper itself lays out critically the drawbacks and limitations which I request you to read. I am mentioning the obvious ones –

  1. good materials but bad visual layout may get rejected or accept crappy papers with good layout may get accepted!
  2. both the classifier and the generative model assume that all the papers have the same typesetting style.

Things to ponder –

After reading the paper(or this blog) if you are wondering what to do next then here are a few tips for you

  • Try building nlp based classifier which checks for content and not just “paper gestalt”.
  • Try using Diverse image-to-image translation via disentangled representations approach to generate diverse good paper visuals
  • Lastly, read the paper! 😉

I hope this was helpful, feel free to ping me if you have any doubts or if you want to catch over coffee!

Happy Coding! 🙂

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