Deep Learning: Interesting Research papers to read
By: Apoorv Yadav
Tags: Deep Learning, Neural Networks, Papers To Read, Data Science, Machine Learning

1. A Neural Algorithm of Artistic Transfer:

You can find the paper here: https://arxiv.org/abs/1508.06576

I feel like I have seen more style transfer guides than any other applications of deep learning. This is an interesting one to read and fairly simple to understand. You take two images and create a third one which is inspired by the first two.

What will you learn? This will help you to understand the concepts of how feature maps, losses, transfer learning work.

You can read through the article on Neural Style Transfer here: https://www.tensorflow.org/tutorials/generative/style_transfer

I would suggest you to write your own notebook as well, tinker with style and content weights as well as the images, it will help you to understand more.

2. Generative Adversarial Network:

You can read the paper here: https://arxiv.org/abs/1406.2661

If you heard about this one, I guess no introduction is needed. This paper demonstrate how one can generate samples of data. This can be widely using in generating data, video games, Animation etc.

What will you learn? This will help you get familiar with generative models, discriminator networks and Deconvolutions.

You can read through the article here: https://www.tensorflow.org/tutorials/generative/dcgan

Optional: if you are unfamiliar with Deconvolutions: https://medium.com/thenoobengineer/demystifying-deconvolution-4909a888c83f

3. Attention Is All You Need:

You can read the paper here: https://arxiv.org/pdf/1706.03762.pdf

If you are familiar with NLP, You must have heard of RNNs, GRUs and LSTMs. This one takes us to next level. I find the paper itself so interesting that you might not even need any supplementary guide to understand. The building blocks explained in this paper — Transformers are now widely used in modern networks.

what will you learn? This will help you to understand the concepts of NLPs, Transformers, Self-Attention. You might even understand this one without reading about GRU and LSTM. (I do suggest reading about them later on since they were widely popular)

You can read through this article here: http://jalammar.github.io/illustrated-transformer/

4. First Order Motion Model for Image Animation:

Video Sequence generated using First Order Motion Model || Source: https://github.com/AliaksandrSiarohin/first-order-model

You can read the paper here: https://arxiv.org/abs/2003.00196

If you have seen people posting with videos captioned as Deepfake and you have always been amazed, this paper is for you. Its a bit complex, However, the authors have kept it self explanatory.

what will you learn? You will get familiar with the concept of Image Animation, Keypoints in Images, Image Translation.

This paper is more difficult than the first three. You might feel different depending on your interests. However you will find it very interesting.

You can read through the article where I have explained the paper here: https://medium.com/thenoobengineer/first-order-motion-model-for-image-animations-f3293c12da96

You can visit the github repo and run the demo and go through the code here: https://github.com/AliaksandrSiarohin/first-order-model

5. Open Pose Estimation:

You can read the paper here: https://arxiv.org/pdf/1812.08008.pdf

Multiperson pose estimation is one of the applications of deep learning. This paper utilizes Affinity Fields and Confidence maps for this. You will find this very intuitive.

What will you learn? You will get familiar with Single Person Pose Estimation, MultiPerson Pose Estimation, Top-Down and Bottom-up Approach, Affinity Fields, etc.

If you would like to read an article explaining the things, https://medium.com/thenoobengineer/openpose-estimation-model-81de994fea69