Are You Ready for Vision Transformer (ViT)?

Are You Ready for Vision Transformer (ViT)?

Are You Ready for Vision Transformer (ViT)? “An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale” May Bring Another Breakthrough to Computer Vision

Lives on earth face a cycle of rise and fall. It is applicable not only for creatures but also for technologies. The technologies in data science have been filled with hypes and biased success stories. Having said that, there are technologies that have lead to the growth of data science: Convolutional Neural Network (CNN). Since AlexNet **in 2012, different architectures of CNNs have brought a tremendous contribution to real business operations and researches in many academic domains. [Residual Networks (ResNet)](https://arxiv.org/abs/1512.03385) by Microsoft Research in 2015 brought a real breakthrough to build “deep” CNNs. However, the retirement of this technology would be approaching. Geoffrey Hinton, a father of neural network and one of the 2018 Turing Award winners, has been mentioning the flaws of CNN for years. You can find one of his seminars “[What is wrong with convolutional neural nets](https://www.youtube.com/watch?v=Jv1VDdI4vy4)?” in 2017. A major flaw of CNN exists in Pooling layers because it loses a lot of valuable information and it ignores the relation between the part of images and the whole. As the replacement of CNN, Geoffrey Hinton and his team had published [a paper on **Capsule Nets](https://openreview.net/forum?id=HJWLfGWRb) in 2018; however, it has not replaced CNNs yet.

Table of Contents

  1. Intro to An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
  2. Why Vision Transformer (ViT) Matters?
  3. Closing
  4. Materials to Study Further

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