Deep Learning models for Image Classification have achieved an exponential decline in error rate through last few years. Since then, Deep Learning has become prime focus area for AI research. However, Deep Learning has been around for a few decades now. Yann Lecun, presented a paper pioneering the Convolutional Neural Networks (CNN) in 1998. But it wasn’t until the start of the current decade that Deep Learning really took off. The recent disruption can be attributed to increased processing power (aka GPUs), the availability of abundant data (aka Imagenet dataset) and new algorithms and techniques. It all started in 2012 with the AlexNet, a large, deep Convolutional Neural Network which won the annual ImageNet Large Scale Visual Recognition Challenge (ILSVRC). ILSVRC is a competition where research teams evaluate their algorithms on the given data set and compete to achieve higher accuracy on several visual recognition tasks.
Since then, variants of CNNs have dominated the ILSVRC and have surpassed the level of human accuracy, which is considered to lie in the 5-10% error range.
For us as humans, it very easy to understand contents of an image. For example, while watching a movie (like Lord of The Rings) I just need to see one example of a Dwarf and that allows me to identify other dwarves without any effort. However, for a machine, the task is extremely challenging because all it can see in an image is an array of numbers. If the task is to identify a cat in an image, you can appreciate the difficulty in finding a cat from this vast array of numbers. Also, cats come in all shapes, sizes, colors and poses, making the task even more challenging.
Based on our experience with Deep Learning for more than four years now, we are listing down some path breaking research papers that are a must-read for anyone associated with computer vision. In this blog-post we focus specifically on image classification and following posts will cover other areas such as object detection and localization.
Also, we have added our two cents about some upcoming algorithms which have the potential to shape the future of computer vision research.