This article was written by Harshvardhan Gupta.
Data driven algorithms like neural networks have taken the world by storm. Their recent surge is due to several factors, including cheap and powerful hardware, and vast amounts of data. Neural Networks are currently the state of the art when it comes to ‘cognitive’ tasks like image recognition, natural language understanding , etc. ,but they don’t have to be limited to such tasks. In this post I will discuss a way to compress images using Neural Networks to achieve state of the art performance in image compression , at a considerably faster speed.
Table of contents
- Enter Convolutional Neural Networks
- The Architecture
- What is the residual?
- The loss functions
- The Training Scheme
- Benchmarks
- Conclusion
To read the whole article, with each point detailed and illustrated, click here.
