![]() This in turn will make the network recognize the bird even more strongly on the next pass and so forth, until a highly detailed bird appears, seemingly out of nowhere. We ask the network: “Whatever you see there, I want more of it!” This creates a feedback loop: if a cloud looks a little bit like a bird, the network will make it look more like a bird. Again, we just start with an existing image and give it to our neural net. ![]() If we choose higher-level layers, which identify more sophisticated features in images, complex features or even whole objects tend to emerge. Right: processed images by Matthew McNaughton, Software Engineer Left: Original painting by G eorges Seurat. Right: processed by Günther Noack, Software Engineer For example, lower layers tend to produce strokes or simple ornament-like patterns, because those layers are sensitive to basic features such as edges and their orientations. Each layer of the network deals with features at a different level of abstraction, so the complexity of features we generate depends on which layer we choose to enhance. We then pick a layer and ask the network to enhance whatever it detected. In this case we simply feed the network an arbitrary image or photo and let the network analyze the picture. Instead of exactly prescribing which feature we want the network to amplify, we can also let the network make that decision. Visualization can help us correct these kinds of training mishaps. Maybe it’s never been shown a dumbbell without an arm holding it. In this case, the network failed to completely distill the essence of a dumbbell. There are dumbbells in there alright, but it seems no picture of a dumbbell is complete without a muscular weightlifter there to lift them. By itself, that doesn’t work very well, but it does if we impose a prior constraint that the image should have similar statistics to natural images, such as neighboring pixels needing to be correlated. Say you want to know what sort of image would result in “Banana.” Start with an image full of random noise, then gradually tweak the image towards what the neural net considers a banana (see related work in, ,, ). One way to visualize what goes on is to turn the network upside down and ask it to enhance an input image in such a way as to elicit a particular interpretation. The final few layers assemble those into complete interpretations-these neurons activate in response to very complex things such as entire buildings or trees. ![]() Intermediate layers interpret the basic features to look for overall shapes or components, like a door or a leaf. For example, the first layer maybe looks for edges or corners. We know that after training, each layer progressively extracts higher and higher-level features of the image, until the final layer essentially makes a decision on what the image shows. One of the challenges of neural networks is understanding what exactly goes on at each layer. The network’s “answer” comes from this final output layer. Each image is fed into the input layer, which then talks to the next layer, until eventually the “output” layer is reached. The network typically consists of 10-30 stacked layers of artificial neurons. We train an artificial neural network by showing it millions of training examples and gradually adjusting the network parameters until it gives the classifications we want. So let’s take a look at some simple techniques for peeking inside these networks. But even though these are very useful tools based on well-known mathematical methods, we actually understand surprisingly little of why certain models work and others don’t. However, images based on places by MIT Computer Science and AI Laboratory require additional permissions from MIT for use.Īrtificial Neural Networks have spurred remarkable recent progress in image classification and speech recognition. under a Creative Commons Attribution 4.0 International License. Images in this blog post are licensed by Google Inc. Posted by Alexander Mordvintsev, Software Engineer, Christopher Olah, Software Engineering Intern and Mike Tyka, Software Engineer
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