Another method that I found to work extremely well in computer vision is transfer learning. Suppose you want to design image understanding software for self-driving cars. The network has entered a local minimum and is unable to get out of it.
Express 16(1), 328 (2008). , Crossref, CAS10. H. Yamada, M. Nozawa, M. Kinoshita, and K. Ohashi, Opt. Common sense might lead you to believe that the neural network would assign an equal share of the probability distribution to our two favorite pet species. The cumulative offset across all layers can be computed by walking the graph backwards. See Figure 7 for an illustration of this behavior. Each convolutional layer can be seen as a set of image filters that trigger a high response on a particular pattern.
You may however find it easier to simply download the pretrained model from the public DIGITS Model Store! For simplifying the measurement of the test photodiodes, they were accessed electrically and optically from the same chip side, namely from the back-end-of-line (BEOL) side. Well that’s simple: in a fully-connected layer, every output neuron computes a weighted sum of the values in the input. In contrast, in a convolutional layer, every filter computes a weighted sum of the values in the receptive field.