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Image Classification and the Problem of Overfeeding

Algorithms and artificial intelligence are always being developed to simplify the process of reading and interpreting data. Data is an infinite resource that requires the work of complex artificial intelligence systems known as neural networks.

What are neural networks?

Recommender systems actively use neural networks and their ability to learn new things over time. They were designed to replicate natural cognitive abilities through a system of logic and reasoning.

Neural networks consist of many layers that work together to correctly evaluate and classify data. The various layers communicate with each other – fulfilling sets of variables – in an attempt to provide an output. The output is the final level and the neural networks final response to the data it was asked to evaluate. Layers are capable of remembering data and try to create patterns and associations based on the data they are fed.

Recommender systems can benefit from these networks, as they will allow them to analyze complex data patterns in an effort to provide useful recommendations that are likely to translate into ROI.

Neural networks and image classification

In recent years, neural networks have been created to process data in innovative and complex ways. Image classification calls upon a neural network to detect specific features in an image. The network is fed with millions of images in order to build a solid base of features and classifications. As levels develop, they begin to master specific features and continue to develop a sophisticated understanding of high-level features.

Put simply, a basic recognition would observe rough or smooth edges, the intermediate stage might detect shapes or larger elements, and the final layer would connect the features into a logical solution. While this process can work in theory, the results can vary and even the most sophisticated algorithms can struggle to interpret the data correctly. In the end, overfeeding becomes a problem as the algorithm tries to connect every element it is asked to detect and process.

Google Take on Image Sorting

Google ran a series of tests that highlighted the problems with data overload, or in its own words: the process of “inceptionism”. In short, inceptionism is the imaginative result of an image classification system being fed an image and interpreting something new from the data it has been asked to process.

The same problem occurs with recommender systems when the system becomes too familiar with data and tries to overcomplicate the data and generate unrealistic recommendations.

The dog knight

Google’s animal detection algorithm was asked to analyze an image of a knight. The neural network specialized in animal detection and had very little experience in image recognition outside of that context. When he processed the image of the knight, he saw colors and patterns he recognized from the millions of animals he had previously analyzed. As the layers communicated, they envisioned strange images of dog heads, noses, eyes, and other strange patterns on the blurred background. The neural network generally worked, but the process of overfeeding saw it overcomplicate and misinterpret the image.

Abstract Cloud Visualizations

For the next set, an abstract image of clouds was fed into the system. The results were similar to the previous knight image. Instead of classifying the image as a set of clouds, the system overcomplicated the process and rendered various animals such as “admiral dog”, “pig-snail”, “camel-bird” and “dogfish”.

“The results are interesting – even a relatively simple neural network can be used to over-interpret an image, just as we enjoyed watching clouds and interpreting random shapes as children. This network was trained mainly on images of animals, so of course it tends to to interpret the shapes as animals. But because the data is stored at such a high abstraction, the results are an interesting remix of these learning features,” Google wrote on its official research blog.

The Imagined Arm

In this example, the neural network associates dumbbells with a hand that lifts them. He had never seen a set of dumbbells without an arm, so the various classification layers built an entire arm to hold the dumbbells based on their understanding that it was a necessary component even when it wasn’t in the original image.

The imaginary banana

The complexity of neural networks can even create images from static noise. Google fed its image classification system with an image of millions of random pixels. The output of the neural network was a fuzzy image of the banana. Why did this happen? As we continue to learn more about these complex systems, we are also learning new ways to trick them into finding features that will prompt the system to recognize an image in a certain way.

The problem with overfeeding

Neural networks have infinite potential, but they will continue to struggle unless algorithms can find a way to deal with the problem of data overload. Layers in a neural network must process the data and make logical conclusions based on data patterns and learning properties. However, a paradox presents itself: as layers become more sophisticated and capable of capturing detailed features, they will also fall victim to overthinking those features, similar to what happened in the images above.

Neural networks are powerful tools that will greatly aid recommender systems as they allow them to build upon a base of recommendations. If the problem of overfeeding can be solved, recommender systems will become increasingly accurate in their outputs and this will greatly improve the user experience.

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