neural networks - How do we combine feature maps? CNN
Nov 21, 2022 · In Convolutional Neural Networks we extract and create abstractified “feature maps” of our given image. My thought was this: We extract things like lines initially. Then from different types of lin...
How to use CNN for making predictions on non-image data?
Feb 7, 2019 · You can use CNN on any data, but it's recommended to use CNN only on data that have spatial features (It might still work on data that doesn't have spatial features, see DuttaA's comment below). For example, in the image, the connection between pixels in some area gives you another feature (e.g. edge) instead of a feature from one pixel (e.g ...
In a CNN, does each new filter have different weights for each …
Typically for a CNN architecture, in a single filter as described by your number_of_filters parameter, there is one 2D kernel per input channel. There are input_channels * number_of_filters sets of weights, each of which describe a convolution kernel. So the diagrams showing one set of weights per input channel for each filter are correct.
When training a CNN, what are the hyperparameters to tune first?
Firstly when you say an object detection CNN, there are a huge number of model architectures available. Considering that you have narrowed down on your model architecture a CNN will have a few common layers like the ones below with hyperparameters you can tweak: Convolution Layer:- number of kernels, kernel size, stride length, padding
convolutional neural networks - When to use Multi-class CNN vs.
Sep 30, 2021 · I'm building an object detection model with convolutional neural networks (CNN) and I started to wonder when should one use either multi-class CNN or a single-class CNN. That is, if I'm making e.g. a
How do I handle large images when training a CNN?
Aug 31, 2017 · Commonly researches scale the images to a resonable size. But if that's not an option for you, you'll need to restrict your CNN. In addition to downsampling in early layers, I would recommend you to get rid of FC layer (which normally takes most of parameters) in favor of convolutional layer. Also you will have to stream your data in each epoch ...
neural networks - Are fully connected layers necessary in a CNN ...
Aug 6, 2019 · A convolutional neural network (CNN) that does not have fully connected layers is called a fully convolutional network (FCN). See this answer for more info. An example of an FCN is the u-net , which does not use any fully connected layers, but only convolution, downsampling (i.e. pooling), upsampling (deconvolution), and copy and crop operations.
How are the kernels initialized in a convolutional neural network?
Sep 29, 2021 · I am currently learning about CNNs. I am confused about how filters (aka kernels) are initialized. Suppose that we have a $3 \\times 3$ kernel. How are the values of this filter initialized before
Is there any difference between ConvNet and CNN?
Feb 15, 2021 · I don't believe there is any particular reason to choose one over the other: ConvNet is slightly easier to say out loud and CNN is slightly shorter to write, but there is absolutely no difference in meaning. For some contrasting examples in the literature, the EfficientNet paper chooses the term ConvNet and this paper on AlexNet chooses CNN ...
What to do if CNN cannot overfit a training set on adding dropout?
Feb 15, 2018 · I have been trying to use CNN for a regression problem. I followed the standard recommendation of disabling dropout and overfitting a small training set prior to trying for generalization. With a 10 layer deep architecture, I could overfit a …