Mixed4c Mixed5b Finally, if we zoom out a little, we can see how the broader shape of the activation space changes from layer to layer. Unfortunately these vectors of activation values are just vectors of unitless numbers and not particularly interpretable by people. Please detail exact equations inline. Topics like network architecture, batch normalization, vanishing gradients, dropout, initialization techniques, non-convex optimization,biases, choices of loss functions, data augmentation,regularization methods, computational considerations, modifications of backpropagation, and more were also not discussed yet. Second, we need to provide enough samples to span the full manifold we want to observe. This means that gradient based approaches for optimisation are not feasible.
What type of contributions does this article make? I think these adequately address the reviewer's concerns. The last layer, however, is an important one and one that we will go into later on. A more principled clustering method might be better. Close up photos of small insects have more opportunity for blurry background foliage than photos of larger animals, like monkeys. But, if we use a different dataset to collect the activations, we could use the atlas as a way of inspecting an unknown dataset.
The idea is that you give the computer this array of numbers and it will output numbers that describe the probability of the image being a certain class. Does the article critically evaluate its limitations? If a plan fails, the pull request will be marked accordingly. Imagenet is used frequently to illustrate visualizations, but different datasets can behave very differently. Roughly speaking, we can think of feature visualization as creating an idealized image of what the network thinks would produce a particular activation vector. Especially when the main message of this work seem to be that the concepts are data-dependent not only by sub-sampling the patches, but it also clearly shows that the discriminability within the dataset is the key. This diversity is a reflection of the variety of abstractions and concepts the model has developed. More theoretical than practical, this activation function mimics the all-or-nothing property of biological neurons.
The reviewer chose to waive anonymity. We could have instead used rank, or a combination of the two, but magnitude will suffice to show us a good variety of concepts. Finally, and most strikingly, it is demonstrated that some individual classifications can be understood as a mere sum of parts. First, I would love to see the method applied on a second or third dataset. The execution of this submission is exceptional. Going Deeper Through the Network Now in a traditional convolutional neural network architecture, there are other layers that are interspersed between these conv layers. How accurate is the model? Aging led to increased transcriptional noise, indicating deregulated epigenetic control.
Since we want to visualize a direction in activation space instead of an individual neuron, this pans out to maximizing a dot product. The Allen Institute for Brain Science is dedicated to accelerating the understanding of how the human brain works in health and disease. Therefore, in practice the tanh units is always preferred to the sigmoid units. These sandy, rocky backgrounds slowly blend into beaches and bodies of water. Facebook and Instagram can use all the photos of the billion users it currently has, Pinterest can use information of the 50 billion pins that are on its site, Google can use search data, and Amazon can use data from the millions of products that are bought every day.
Experiments are thoughtfully designed, paper errs towards humility, notes potential weaknesses. What if we want to understand how it reacts to millions of images? As a suggestion, I think the tone of the article could still be muted a bit, to reflect the exploratory nature of this research. By artfully merging several techniques, it uncovers genuinely new insights about what a state-of-the-art image classifier is doing inside. I notice that this correlates to the number of activation vectors used in the average. I wonder specifically about Resnet, since it is so effective and so commonly used; and I also wonder if some of the effects go away or change significantly when the architecture are applied on closely related domains like image segmentation. We want to get to a point where the predicted label output of the ConvNet is the same as the training label This means that our network got its prediction right.
It is briefly discussed in the code comments of the included notebook. It is not saying that this method might not become a useful tool for e. In particular, large negative numbers become 0 and large positive numbers become 1. Without selection and only projection, a network will thus remain in the same space and be unable to create higher levels of abstraction between the layers. How readable is the paper, accounting for the difficulty of the topic? Also, the attributions for the earlier layers do not make much sense so it might be useful to describe why is that the case. Now this filter is also an array of numbers the numbers are called weights or parameters.
But there was still a problem — what combinations of neurons should we be studying? Can they do it more successfully with the visualization than they can without? The paper's beautiful visualizations uncover several terrifically interesting ideas. We suspect it is because the data is much more sparse. An example is shown at the top of this article. These should all be polished up now, thanks! We can mix together a couple hundred basis neurons to get any activation vector. Types of these features could be semicircles combination of a curve and straight edge or squares combination of several straight edges. The representative array will be 480 x 480 x 3.
If we zoom in, we can get a better look at what distinguishes the two classifications at this layer. Moving upward, we see many variations of people. Similarly, for the whole section which investigates the manifold, it is important to note that those are just post-hoc observations and that the curved paths are just intriguing observations on this particular projection. They detect low level features such as edges and curves. Because we are visualizing a specific activation vector, it is only looking at one. The later layers also tend to have larger receptive fields than the ones that precede them meaning they are shown larger subsets of the image so the concepts seem to encompass more of the whole of objects.
Journal of Machine Learning Research, Vol 9 Nov , pp. How easy would it be to replicate or falsify the results? Communication: Is the article well-organized, focused and structured? For example, no human who recognizes the difference between a pan and a wok would call a pan a wok just because it's next to noodles. How do the filters in the first conv layer know to look for edges and curves? The submission briefly mentions these in a technical aside. How easily would a lay person understand them? Zan Armstrong helped with the interactive diagrams and the writing. We can have other filters for lines that curve to the left or for straight edges. Does the article cite relevant work? Examples are shown of creating fooling examples to push a network to related neighbor classes. The lung aging atlas can be accessed via an interactive user-friendly.