Introduction
It is becoming very hard to stay up to date with recent advancements happening in deep learning. Hardly a day goes by without a new innovation or a new application of deep learning coming by. However, most of these advancements are hidden inside the large amount of research papers that are published on mediums like ArXiv / Springer
To keep ourselves updated, we have created a small reading group to share our learnings internally at Analytics Vidhya. One such learning I would like to share with the community is a a survey of advanced architectures which have been developed by the research community.
This article contains some of the recent advancements in Deep Learning along with codes for implementation in keras library. I have also provided links to the original papers, in case you are interested in reading them or want to refer them.
To keep the article concise, I have only considered the architectures which have been successful in Computer Vision domain.
If you are interested, read on!
P.S.: This article assumes the knowledge of neural networks and familiarity with keras. If you need to catch up on these topics, I would strongly recommend you read the following articles first:
Table of Contents
What do we mean by an advanced architecture?
Types of Computer Vision Tasks
List of Deep Learning Architectures
What do we mean by an Advanced Architecture?
Deep Learning algorithms consists of such a diverse set of models in comparison to a single traditional machine learning algorithm. This is because of the flexibility that neural network provides when building a full fledged end-to-end model.
Neural network can sometimes be compared with lego blocks, where you can build almost any simple to complex structure your imagination helps you to build.
We can define an advanced architecture as one that has a proven track record of being a successful model. This is mainly seen in challenges like ImageNet, where your task is to solve a problem, say image recognition, using the data given. Those who don’t know what ImageNet is, it is the dataset which is provided in ILSVR (ImageNet Large Scale Visual Recognition) challenge.
Also as described in the below mentioned architectures, each of them has a nuance which sets them apart from the usual models; giving them an edge when they are used to solve a problem. These architectures also fall in the category of “deep” models, so they are likely to perform better than their shallow counterparts.
Types of Computer Vision Tasks
This article is mainly focused on Computer Vision, so it is natural to describe the horizon of computer vision tasks. Computer Vision; as the name suggests is simply creating artificial models which can replicate the visual tasks performed by a human. This essentially means what we can see and what we perceive is a process which can be understood and implemented in an artificial system.
The main types of tasks that computer vision can be categorised in are as follows:
Object Recognition / classification – In object recognition, you are given a raw image and your task is to identify which class does the image belong to.
Classification + Localisation – If there is only one object in the image, and your task is to find the location of that object, a more specific term given to this problem is localisation problem.
Object Detection – In object detection, you task is to identify where in the image does the objects lies in. These objects might be of the same class or different class altogether.
Image Segmentation – Image Segmentation is a bit sophisticated task, where the objective is to map each pixel to its rightful class.
List of Deep Learning Architectures
Now that we have understood what an advanced architecture is and explored the tasks of computer vision, let us list down the most important architectures and their descriptions:
链接：
https://www.analyticsvidhya.com/blog/2017/08/10-advanced-deep-learning-architectures-data-scientists/
原文链接：
