One of the most common tasks that neural networks have to solve is recognizing visual images. Currently, machines are tasked with recognizing characters on paper, signatures on documents, images in photographs or video from surveillance cameras in real time. High efficiency in performing these tasks allows people to significantly simplify their work, as well as reduce the likelihood of human error. But how does a neural network cope with this task, and does it really perform better than a person?
Contents
- 1 Application of neural networks in image recognition
- 2 How neural networks are trained
- 3 How neural networks recognize images
- 4 Why neural networks recognize images more effectively than people
Application of neural networks in image recognition
The Midjourney neural network and all its analogues, such as Kandinsky, can not only generate their own images, but can also “finish drawing” ready-made ones. To solve this problem and harmoniously complement the picture, it must first be recognized. Most people use such neural networks simply for fun.
However, the ability to recognize images, as mentioned above, can be used to solve more important problems. For example, one of the most important areas of AI application, according to experts, is the structuring of images received from all the cameras in the world. These video streams are a library of unstructured data, so there is little practical use for it.
But, by using artificial intelligence, all this data can be structured, despite the fact that we are talking about a colossal amount of information. As a result, the library can be used for a variety of purposes, from household to professional and government, including security.
How neural networks are trained
Before solving any problem, any neural network must undergo training. Moreover, it does not matter what task we are talking about – recognizing or generating images, writing texts, as in the case of GPT-4, or even generating music. That is, the work of AI resembles the work of the human brain. For example, we analyze an image and identify it based on existing knowledge.
For this reason, neural networks are demanding of the dataset, that is, the quality and volume of data on which they are trained. As a rule, the dataset is taken from open sources. At the same time, it is always important that the initial data for the neural network is unambiguous and consistent.
It should be said that there are different strategies for training AI, but they all boil down to the fact that neural networks are provided with a dataset for study. At the same time, AI can immediately be told what the correct answer should be or not be told at all, so that the neural network itself gives an answer based on its own analysis of certain features. Sometimes different training strategies are combined.
To make it easier to understand how a neural network works and how it learns, it can be represented as a tree, where each branch is a possible answer. Each branch has a different thickness, or different “weight”, but all branches are interconnected. During the learning process, the neural network analyzes the degree of influence of one “branch” on another. The most frequent results have a greater “weight”, that is, a numerical coefficient that is assigned during the learning process, and which the neural network focuses on when issuing a result.
When neural networks are trained to recognize images, they are given various samples with a label indicating what type they belong to. The samples are certain features of the image, from which possible answer options arise, that is, the above-mentioned “branches”. The set of features allows the neural network to clearly determine what class of images it is dealing with. Therefore, during the training process, the neural network must learn to work with a sufficient number of features to recognize unknown images with high accuracy.
How a neural network recognizes an image
A trained neural network has a good stock of knowledge that allows it to recognize an image. How is this implemented in practice? The image is divided into small sections, down to groups of several pixels, and then hundreds of thousands of such groups are compared with known images and analyzed for known features. Simply put, artificial intelligence compares the image (its individual parts) with the base on which it was trained and looks for matches.
After the neural network recognizes objects in the image, it assigns them a class. For example, in a photo of a person sitting on a sofa with a cat in his arms, the neural network distinguishes each object separately, that is, the sofa, the person, the cat, and even the clothes on the person. All these objects belong to different classes. Subsequently, when the image is recognized, the neural network can perform further actions with it, for example, draw a more meaningful image. In the case of video surveillance, the neural network first recognizes the image, that is, the objects on it, and then determines the actions and classifies them.
From all of the above, it follows that the more features the neural network knows, the more accurate the result. However, at some point, memorizing features turns into simply memorizing a sample. Therefore, in order to produce good accuracy, it is important for the neural network not to “overtrain”, otherwise it will simply adjust to the training sample.
Why are neural networks more effective at recognizing images than people
Why can a neural network cope with this task more effectively than a person? First of all, as already mentioned above, the human factor is excluded. For example, a person can get distracted, make a mistake due to fatigue, etc. In addition, AI can work much faster and with a much larger amount of data.
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This applies not only to image recognition, but also to many other tasks. For example, this is the reason why neural networks can make medicines cheaper and more accessible. For example, COVID-19 vaccines were developed using neural networks, which significantly accelerated their development.