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Of course, activation atlases do have limitations. For example, by looking at an activation atlas we will be able to see why a picture of a baseball can switch the classification of an image from “grey whale” to “great white shark”. These atlases not only reveal visual abstractions within a model, but later in the article we will show that they can reveal high-level misunderstandings in a model that can be exploited. Similarly, activation atlases give us a bigger picture view by showing common combinations of neurons. As an analogy, while the 26 letters in the alphabet provide a basis for English, seeing how letters are commonly combined to make words gives far more insight into the concepts that can be expressed than the letters alone. In practice, however, neurons are rarely used by the network in isolation, and it may be difficult to understand them that way. In theory, showing the feature visualizations of the basis neurons would give us the global view of a network that we are seeking.
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By combining these two techniques, we can get the advantages of each in one view - a global map seen through the eyes of the network. (An example is shown at the top of this article.) Broadly speaking, we use a technique similar to the one in CNN codes, but instead of showing input data, we show feature visualizations of averaged activations. In this article we introduce activation atlases to this quiver of techniques. This reveals a broader picture of what the neuron detects but is still focused on individual neurons. Generating diverse starting points for the optimization process by clustering images in the t-SNE map. Nguyen, et al use t-SNE to make more diverse neuron visualizations, Showing which images the model sees as similar does help us infer some ideas about what features the network is responding to,īut feature visualization makes those connections much more explicit. Gives a global view of a dataset by taking each image and organizing them by their activation values from a neural network. There are techniques which give a more global view, but they tend to have other downsides.įor example, Karpathy’s CNN codes visualization When what we want is a map of an entire forest, inspecting one tree at a time will not suffice. Because of this, it doesn’t give us a big picture view of the network. Unfortunately, visualizing activations has a major weakness - it is limited to seeing only how the network sees a single input. Feature visualization addresses this by connecting hidden layers back to the input, making them meaningful. These layers are the heart of how neural networks outperform more traditional approaches to machine learning and historically, we’ve had little understanding of what happens in them With the exception of the first hidden layer. These approaches are exciting because they can make the hidden layers of networks comprehensible. But there was still a problem - what combinations of neurons should we be studying? A natural answer (foreshadowed by work on model inversion ) is to visualize activations, the combination of neurons firing in response to a particular input. Because neurons don’t work in isolation, this led to applying feature visualization to simple combinations of neurons.
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It began with research into visualizing individual neurons and trying to determine what they respond to. This raises a natural question: What have these networks learned that allows them to classify images so well?įeature visualization is a thread of research that tries to answer this question by letting us “see through the eyes” of the network.
#3d tooth atlas activation code mac os x#
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The 3D Tooth Atlas, has been integrated into the curriculum of more than 70% of North American dental schools, and is in use throughout the international dental community as a state-of-the-art professional and patient education solution.įeaturing 3D sections on Periodontology, Odontogenesis, Anthropology, Dental Embryology, and Clinical Access.