machine learning - What is a fully convolution network? - Artificial ...
Jun 12, 2020 · Fully convolution networks A fully convolution network (FCN) is a neural network that only performs convolution (and subsampling or upsampling) operations. Equivalently, an …
Why FCNN is not always better than CNN? - Artificial Intelligence …
Feb 17, 2023 · 0 Why Fully-Connected Neural Network is not always better than Convolutional Neural Network? FCNN is easily overfitting due to many params, then why didn't it reduce the …
neural networks - How can the FCNN reduce the dimensions of …
Jul 20, 2020 · How can the FCNN reduce the dimensions of the input from $1048 \times 100$ to $523 \times 100$ with max-pooling? Ask Question Asked 5 years, 4 months ago Modified 5 …
Are fully connected layers necessary in a CNN?
Aug 6, 2019 · I have implemented a CNN for image classification. I have not used fully connected layers, but only a softmax. Still, I am getting results. Must I use fully-connected layers in a CNN?
What are standard datasets for fully connected neural networks?
I am looking for datasets that are used as a testing standard in the fully connected neural networks (FCNN). For example, in the image recognition and CNN, CIFAR datasets are used …
Concatenation of Feature vectors in transformers before passing …
Aug 17, 2023 · Concatenation of Feature vectors in transformers before passing to fcnn Ask Question Asked 2 years, 4 months ago Modified 2 years, 4 months ago
What is the difference between a convolutional neural network …
Mar 8, 2018 · TLDR: The convolutional-neural-network is a subclass of neural-networks which have at least one convolution layer. They are great for capturing local information (e.g. …
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 …
How to handle rectangular images in convolutional neural …
If you have a rectangular image and you are using existing models (or existing code), then you have to add an input pre-processing pipeline which transforms the image to standard …
How many parameters are being optimised over in a simple CNN?
Dec 26, 2019 · Okay so here's my CNN (simple example from a tutorial) along with some arithmetic to get the total number of free parameters. We've got a dataset of 28*28 grayscale …