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One approach to understanding neural networks, both in neuroscience and deep learning, is to investigate the role of individual neurons, especially those which are easily interpretable.  Our investigation into the importance of single directions for generalisation, soon to appear at the Sixth International Conference on Learning Representations (ICLR), uses an approach inspired by decades of experimental neuroscience — exploring the impact of damage — to determine: how important are small groups of neurons in deep neural networks?

DeepMind is Using ‘Neuron Deletion’ to Understand Deep Neural Networks

In a blog post, researchers at DeepMind have explained how they went about understanding and judging the performance of a neural network by deleting individual neurons one by one, as well as in groups.

Deep neural networks have been hard to interpret so this is a nice start towards demystifying them by one of the leading research companies.Their results imply that individual neurons are much less important than we would have initially thought.

MIT researchers can now track AI’s decisions back to single neurons

AI researchers had a breakthrough when they became able to practically replicate in their machines what we believe to be one of the most basic functions of the human brain: thought is generated by the combined activity of clusters of connected neurons.

That could help us more easily figure out, for example, why a self-driving car swerved off the road after perceiving a certain object, or investigate exactly how biased an image classification algorithm was trained to be.

If representations in deep neural networks are disentangled, we’d have a shot at isolating the specific neurons responsible for identifying, for example, a person’s gender in a photo.

The MIT team wanted to find exactly how different neural networks, trained on different data, built their own mechanisms for understanding concepts, ranging from simple patterns to specific objects.

Since the MIT team knew exactly what the neural network was perceiving in every part of the picture, they would be able to analyze which neurons were highly active at a specific time, and trace the recognition of specific concepts in the picture back to those neurons.

There are conferences for researchers to gather and talk about how to better explain AI, dozens of papers published each year, and even a DARPA program to further deep learning explainability—it all speaks to the work’s importance.

The ability to measure bias in neural networks could be critical in fields like healthcare, where bias inherent in an algorithm’s training data could be carried into treatment, or in determining why self-driving cars make certain decisions on the road for a safer vehicle.

What’s New in Deep Learning Research: Understanding How Neural Networks Think

One of the challenging elements of any deep learning solution is to understand the knowledge and decisions made by deep neural networks.

While the interpretation of decisions made by a neural networks has always been difficult, the issue has become a nightmare with the raise of deep learning and the proliferation of large scale neural networks that operate with multi-dimensional datasets.

For instance, feature visualization is a very effective technique to understand the information processed by individual neurons but fails to correlate that insight with the overall decision made by the neural network.

Combining those building blocks, Google has created an interpretability models that does not only explains what a neural network detects, but it does answer how the network assembles these individual pieces to arrive at later decisions, and why these decisions were made.

The research group accompanied the paper with the release of Lucid, a neural network visualization library that allow developers to make the sort lucid feature visualizations that illustrate the decisions made by individual segments of a neural network.

Understanding Neural Networks Through Deep Visualization

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.

Next, we do a forward pass using this image \(x\) as input to the network to compute the activation \(a_i(x)\) caused by \(x\) at some neuron \(i\) somewhere in the middle of the network.

At the end of the backward pass we are left with the gradient \(\partial a_i({x}) /\partial {x}\), or how to change the color of each pixel to increase the activation of neuron \(i\).

Deep networks are easily fooled: high confidence predictions for unrecognizable images, the results when this process is carried out on final-layer output neurons are images that the DNN thinks with near certainty are everyday objects, but that are completely unrecognizable as such.

To produce more recognizable images, researchers have tried optimizing images to (1) maximally activate a neuron, and (2) have styles similar to natural images (e.g.

Instead of being comprised of clearly recognizable objects, they are composed primarily of “hacks” that happen to cause high activations: extreme pixel values, structured high frequency patterns, and copies of local motifs without global structure.

2014 provided a great explanation for how such adversarial and fooling examples are to be expected due to the locally linear behavior of neural nets.

In the supplementary section of our paper (linked at the top), we give one possible explanation for why gradient approaches tend to focus on high-frequency information.

Because the optimization is stochastic, by starting at different random initial images, we can produce a set of optimized images whose variance provides information about the invariances learned by the unit.

Our paper describes a new, open source software tool that lets you probe DNNs by feeding them an image (or a live webcam feed) and watching the reaction of every neuron.

Imagine that, to preserve your privacy, the phone or robot does not explicitly store any pictures or videos obtained during operation, but they do use visual input to train their internal network to better perform their tasks.

The above results show that given only the learned network, one may still be able to reconstruct things that you would not want released, such as pictures of your valuables, or your bedroom, or videos of what you do when you are home alone, such as walking around naked singing Somewhere Over the Rainbow.

They also build on the idea in our paper and in Mahendran and Vedaldi (2014) that stronger natural image priors are necessary to produce better visualizations with this gradient ascent technique.

Their prior that 'neighboring pixels be correlated' is similar both to one of our priors (Gaussian blur) and to Mahendran and Vedaldi's minimization of “total variation”, emphasizing the likely importance of these sorts of priors in the future!

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