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The neurons selected for these images are the output neurons that a DNN uses to classify images as flamingos or school buses. Below we show that similar images can be made for all of the hidden neurons in a DNN.
Our paper describes that the key to producing these images with optimization is a good natural image prior. Background Deep neural networks have recently been producing amazing results!
But how do they do what they do? Recently, we and others have started shinning light into these black boxes to better understand exactly what each neuron has learned and thus what computation it is performing.
Here we will focus on images, but the approach could be used for any modality.
This approach has been around for quite a while and was re- popularized by Erhan et al. The image will initially look like colored TV static: But what types of images does this process produce?
As we showed in a previous paper titled Deep networks are easily fooled: The DNN is near-certain all of these images are examples of the listed classes over Images are produced with gradient-based optimization top or two different, evolutionary optimization techniques bottom.
Images from Nguyen et al. Images produced with weak regularization: These images are produced with the same gradient-ascent optimization technique mentioned above, but using only L2 regularization.
From Simonyan et al. Adding regularization thus helps, but it still tends to produce unnatural, difficult-to-recognize images. In addition to Simonyan et al. In the supplementary section of our paper linked at the topwe give one possible explanation for why gradient approaches tend to focus on high-frequency information.
The figure at the top of this post showed some examples for ImageNet classes, and here is the full figure from the paper. You can also browse all fc8 visualizations on one page.
Synthetic images produced with our new, improved priors to cause high activations for different class output neurons e. The different types of images in each class represent different amounts of the four different regularizers we investigate in the paper. For all other classes, we have selected four interpretable examples.
For example, the lower left quadrant tends to show lower frequency patterns, the upper right shows high frequency patterns, and the upper left shows a sparse set of important regions.But while well-presented tables and figures in research papers can efficiently capture and present information, poorly crafted tables and figures can confuse readers and impair the effectiveness of a paper.
16 To help authors get the balance right, this article presents some essential guidelines to the effective use of tables and figures in.
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Dear Twitpic Community - thank you for all the wonderful photos you have taken over the years. We have now placed Twitpic in an archived state. The Purdue University Online Writing Lab serves writers from around the world and the Purdue University Writing Lab helps writers on Purdue's campus.