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Jan 24, 2019 · The convolutional layers and pooling layers themselves are independent of the input dimensions. However, the output of the convolutional layers will have different spatial sizes for differently sized images, and this will cause an issue if we have a fully connected layer afterwards (since our fully connected layer requires a fixed size input).
Convolutional neural nets are a specific type of deep neural net which are especially useful for image recognition. Specifically, convolutional neural nets use convolutional and pooling layers, which reflect the translation-invariant nature of most images. For your problem, CNNs would work better than generic DNNs since they implicitly capture ...
Dec 25, 2015 · Filter consists of kernels. This means, in 2D convolutional neural network, filter is 3D. Check this gif from CS231n Convolutional Neural Networks for Visual Recognition: Those three 3x3 kernels in second column of this gif form a filter. So as in the third column. The number of filters always equal to the number of feature maps in next layer.
152. In recent years, convolutional neural networks (or perhaps deep neural networks in general) have become deeper and deeper, with state-of-the-art networks going from 7 layers (AlexNet) to 1000 layers (Residual Nets) in the space of 4 years. The reason behind the boost in performance from a deeper network, is that a more complex, non-linear ...
May 22, 2015 · In the neural network terminology: one epoch = one forward pass and one backward pass of all the training examples. batch size = the number of training examples in one forward/backward pass. The higher the batch size, the more memory space you'll need. number of iterations = number of passes, each pass using [batch size] number of examples.
I don't have computer vision background, yet when I read some image processing and convolutional neural networks related articles and papers, I constantly face the term, translation invariance, or
Dec 8, 2014 · Because we are only interested in the 1 000 000 bits of entropy at the output, we can say that with 1 000 000 parameters, each parameter represents a single bit, which is 1e-4 bit per sample. This means you would need more data. Or you have too much parameters, because e.g. with 100 parameters, you have 10 000 bits per parameter and therefore 1 ...
Sep 14, 2016 · Convolutional Neural Networks (CNNs) are one of the most popular neural network architectures. They are extremely successful at image processing, but also for many other tasks (such as speech recognition, natural language processing, and more). The state of the art CNNs are pretty deep (dozens of layers at least), so they are part of Deep Learning.
Oct 28, 2014 · For CNNs, I think it means the invariance to small* displacements of the input image. For example in the character recognition task, if you train the system by shifting (i.e. sliding the images to left/right and up/down) a little bit, you learn a more generalizable detector, that works under difficult conditions, i.e. when the character is not perfectly aligned to the center of the image.
4. Neural Network Training Is Like Lock Picking. To achieve state of the art, or even merely good, results, you have to set up all of the parts configured to work well together. Setting up a neural network configuration that actually learns is a lot like picking a lock: all of the pieces have to be lined up just right.
