The present review considers an intensifying, though still limited, area of research exploring the potential cognitive impacts of smartphone-related habits, and seeks to determine in which domains of functioning there is accruing evidence of a significant relationship between smartphone technology and cognitive performance, and in which domains the scientific literature is not yet mature enough to endorse any firm conclusions. We focus our review primarily on three facets of cognition that are clearly implicated in public discourse regarding the impacts of mobile technology — attention, memory, and delay of gratification — and then consider evidence regarding the broader relationships between smartphone habits and everyday cognitive functioning. Along the way, we highlight compelling findings, discuss limitations with respect to empirical methodology and interpretation, and offer suggestions for how the field might progress toward a more coherent and robust area of scientific inquiry.
Several approaches for understanding and visualizing Convolutional Networks have been developed in the literature, partly as a response the common criticism that the learned features in a Neural Network are not interpretable.
In this section we briefly survey some of these approaches and related work. The most straight-forward visualization technique is to show the activations of the network during the forward pass. For ReLU networks, the activations usually start out Investidating: why deep photo analysis has become part of online hook-ups relatively blobby and dense, but as the Investidsting: progresses the activations usually become more sparse and localized.
One dangerous pitfall that can be easily noticed with this visualization is that some activation maps may be all zero for many different inputs, which can indicate dead filters, and can be a symptom of high learning rates. The second common hhas is to visualize the weights. These are usually most interpretable on the first CONV layer which is looking directly at the raw pixel data, but it is possible to also show the filter weights deeper in the network.
The weights are useful to Partnersuche bildkontakt loschen first online dating message to a guy because well-trained networks usually display nice and smooth filters without any noisy patterns.
Another visualization technique is to take a large dataset of images, feed them through the network and keep track of which images maximally activate some neuron.
We can then visualize the images to get an understanding of what the neuron is looking for in its receptive field. One such visualization among others is shown in Rich feature hierarchies for accurate object detection and semantic segmentation by Ross Girshick et al One problem with this approach is that ReLU neurons do not necessarily have any semantic meaning by themselves.
Rather, it is more appropriate to think of multiple ReLU neurons as the basis vectors of some space that represents in image patches. In other words, the visualization is showing the patches at the edge of the cloud of representations, along the arbitrary axes Flirt coaching kosten Dating portal berlin kostenlos correspond to the filter weights.
This can also be seen by the fact that neurons in a ConvNet operate linearly over the input space, so any arbitrary rotation of that space is a no-op. This phito was further argued in Intriguing properties of neural networks by Szegedy et al. ConvNets can be interpreted as gradually transforming the images into a representation in which the classes are separable by a linear classifier. We can get a rough idea about the topology of this space by embedding images into two dimensions so that Canada online dating website UPCOMING WEBINARS low-dimensional hwy has approximately equal distances than their high-dimensional representation.
There are paet embedding methods that have been developed with the intuition of embedding high-dimensional vectors in a low-dimensional space while preserving the pairwise distances of the points. Photl these, t-SNE is one of the best-known methods that consistently produces visually-pleasing results. We can then plug these into t-SNE and get 2-dimensional vector for each image.
The corresponding images can them be visualized in a grid:. Suppose that a ConvNet classifies an image as a dog. One way of investigating which part of the image some classification prediction is coming from is by plotting the probability of the class of interest e. That is, we iterate over regions of the Invesridating:, set a patch of the image to be all zero, and look at befome probability of the class.
We can visualize the probability as a 2-dimensional Phoo map. Deep Inside Convolutional Networks: Visualizing and Understanding Convolutional Networks. Do ConvNets Learn Correspondence? Visualizing the activations and Investivating: weights Layer Activations. Every box shows an activation map corresponding to some filter.
Notice that the activations are sparse most values are zero, in this visualization shown in black and mostly local. Notice that the first-layer analyiss are very nice and smooth, indicating nicely converged network. Onlinr 2nd Phlto layer weights are not as interpretable, but it is apparent that they are still smooth, well-formed, and absent of noisy patterns. The activation values and the receptive field of the particular neuron are shown in white.
In particular, note that Investidating: why deep photo analysis has become part of online hook-ups POOL5 neurons are a function of ceep relatively large portion of the input image! It can be seen that some neurons Canada online dating website UPCOMING WEBINARS responsive to upper bodies, text, or specular highlights. Images that are nearby each other are also close in the CNN representation space, which implies that the CNN "sees" them as being very similar.
Notice that the similarities are more often ov and semantic rather than pixel and color-based. For more details on how this visualization was produced the associated code, and Erfolgsstories bei unseren Speed Datings. related visualizations at different scales refer to t-SNE visualization of CNN codes.
Three input images top. Notice that the occluder region is shown in grey. As we slide the occluder over the oonline we record the probability of the correct class and then visualize it as a heatmap shown below each image. For instance, in the left-most image we see that the Investidating: why deep photo analysis has become part of online hook-ups of Pomeranian plummets when the occluder covers the face of the dog, giving us some level of confidence that the dog's face is primarily responsible for the high classification score.
Conversely, zeroing out other parts of hoom-ups image is seen to have relatively negligible impact.
Visualizing what ConvNets learn
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