Convolutional Neural Networks (CNNs) have revolutionised the field of artificial intelligence, particularly in the realm of computer vision. These deep learning models have demonstrated remarkable capabilities in understanding and processing visual data, leading to significant advancements in image recognition,...
CNNs adopt local connection and weight sharing to reduce the storage space of the network model and improve the computing performance. (1) Local connection Full connections are adopted in MLPs, meaning that almost the whole image is processed by each neuron. As shown in Fig. 1.15, neuron 1 ...
In this article, we’ll focus on strided convolutions which improve the conventional convolutional applied in CNNs. Specifically, conventional convolution uses a step size (or stride) of 1 meaning that the sliding filter moves 1 sample (e.g. pixel in the case of images) at a time. On th...
Furthermore, some biological neurons with the same receptive field can only detect lines of different orientations, an example offiltering. Some neurons also have larger receptive fields, meaning that higher-level neurons are a combination of lower-level neurons. ...
We canprove this theoremwith advanced calculus, that uses theorems I don't quite understand, but let's think through the meaning. Because $F(s)$ is the Fourier Transform of $f(t)$, we can ask for a specific frequency ($s = 2\text{Hz}$) and get thecombined interactionof every data...
However, the physical meaning of frequency statistics is not equivalent to that of the spatial domain. For example, the mean of the spectrum is deter- mined by the value of the upper left pixel (fundamental fre- quency) of the spatial image as shown in figure 4. From this...
The interesting part of deep CNN is that deep hidden layer can receive more information from input than shallow layer, meaning although the direct connection is sparse, the deeper hidden neuron are still able to receive nearly all the features from input. ...
If T does not have bounded support, but S does, it is still possible to attach a meaning to the expression S((φT) as follows. Since φT is of class C∞ it is enough to show that any distribution S with bounded support can be extended to the set of all real-valued functions of...
(Though not compared to the regular convolution, it is just used as a simple example but doesn't lend itself well to group convolutions as orientation matters in MNIST images.) Overview Convolutions are equivariant to translations, meaning if we have a convolutional layer L, an input x and ...
As shown in Fig. 1, the training-time RepMLP has FC, conv, and BN layers but can be equivalently converted into an inference-time block with only three FC layers. The meaning of structural re-parameterization is that the training-time model has a set of parameters while the inference-time...