What is the difference between a convolutional neural network …
Mar 8, 2018 · A CNN, in specific, has one or more layers of convolution units. A convolution unit receives its input from multiple units from the previous layer which together create a proximity. …
What is the fundamental difference between CNN and RNN?
A CNN will learn to recognize patterns across space while RNN is useful for solving temporal data problems. CNNs have become the go-to method for solving any image data challenge while …
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. …
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 * …
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 …
Reduce receptive field size of CNN while keeping its capacity?
Feb 4, 2019 · One way to keep the capacity while reducing the receptive field size is to add 1x1 conv layers instead of 3x3 (I did so within the DenseBlocks, there the first layer is a 3x3 conv …
CNN Architectures for local features vs global context
Oct 27, 2021 · There are CNN architectures that, in parallel, use different scales of local features, such as the Inception architecture and ResNext; Both combine local features on different …
What is a cascaded convolutional neural network?
To realize 3DDFA, we propose to combine two achievements in recent years, namely, Cascaded Regression and the Convolutional Neural Network (CNN). This combination requires the …
How to use CNN for making predictions on non-image data?
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 …
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 …