How Do Neural Networks and Capsule Networks Compare?

Deep learning and neural networks have gained massive popularity and achieved remarkable success in recent times, thus becoming the most frequently utilised machine learning models. Convolutional Neural Networks (CNNs) are predominantly used in the fields of image recognition and computer vision. In contrast, Capsule Networks (CapsNets) offer a more organised strategy when compared to convolutional neural networks.

In this article, we shall examine CapsNet and assess its performance in comparison to convolutional neural networks (CNNs).

The Automated Feature and Pattern Extraction Method Employed by CNN

Photograph feature extraction was a laborious and inefficient task before the creation of convolutional neural networks (CNNs). However, CNNs resolved this problem by automating and simplifying the time-consuming process of recognising patterns and features in images. With the aid of advanced linear algebra and matrix multiplication principles, CNNs became one of the most efficient feature extraction methods.

Despite the significant expenses associated with the technology required for Convolutional Neural Networks (CNNs) to operate at optimal levels, such as Graphic Processing Units (GPUs), the investment has undoubtedly been worthwhile. CNNs have resulted in exceptional advancements in various fields, including but not limited to facial recognition, autonomous driving, natural language processing, object identification, and cancer diagnosis.

However, just like any other tech development, CNN has also revealed a vulnerability in its extensive use, according to researchers.

CNN’s Central drawbacks are Max Pooling and the Loss of Spatial Information

In order to diagnose the problem, it is necessary to comprehend the inner processes of CNN.

Convolutional Neural Network (CNN) can extract image patterns and features. This feature extraction is further processed by an algorithm to classify objects present in the image. Computers perceive an image as a matrix with relative intensities of the three primary colours (Red, Green and Blue) at each pixel location. Therefore, pictures can be represented mathematically as simple matrices.

Convolutional Neural Networks (CNNs) are remarkable machine learning instruments trained to leverage the unique traits of each image and activate different convolutional areas. An activation function and a convolutional neural network are employed to generate feature maps for each of these features. This methodology aids in the identification of image features, enabling predictions or classification of objects.

When conducting high-level feature extraction, max pooling is a strategy implemented to downsample feature maps. This technique selects the maximum value from a designated feature map patch, thus reducing its size. This compression of data via max pooling ensures a negligible decrease in accuracy.

We can consider the other issue of CNN by examining an illustration. According to research, the average human face has two eyes, a nose, and a mouth.

The Convolutional Neural Network (CNN) model’s confidence level in identifying an image as a face is high, as different aspects of the face activate distinct areas of the network. We can now examine the outcomes of inputting this picture into the CNN model.

Even though this picture is not of a real person, it can be perceived as a face by a computer since it contains all the standard facial components, including two eyes, a nose and a mouth.

Convolutional Neural Networks (CNNs) are programmed to detect specific image features, irrespective of their location or positional data. The absence of spatial awareness presents a challenge for CNNs to differentiate between human and non-human facial features accurately.

In 2017, Hinton, Sara Sabour, and Nicholas Frosst released a research paper titled “Dynamic Routing Between Capsules“, which presented the idea of capsule networks (CapsNet). The authors introduced a training approach and aimed to address a specific problem.

CapsNet: Overcoming the Limitations of CNN

The creation of artificial neural networks is influenced by the neurons present in the human brain. Hinton and Sabour not only drew inspiration from the human brain, but also from individual “capsules” or brain regions, which assisted in comprehending the intricacies of the brain and how it functions.

Despite the risk of losing information brought on by pooling procedures, the team managed to incorporate the capsule network concept and dynamic routing algorithms with great success. This enabled them to precisely estimate and extract spatial characteristics such as size, orientation, and relative location.

When examining the differences between Capsule Networks (CapsNet) and traditional Convolutional Neural Networks (CNNs), CapsNet’s use of vectors instead of scalars to represent data sets it apart. A comparison of the two networks illustrates their distinct operating methods. The use of vectors, which offer a more accurate representation of the data, enables CapsNet to obtain a more precise understanding of the data and, consequently, greater efficiency.


  • The input scalars are assigned weights, and the two are multiplied.
  • The summation of weighted scalars.
  • The type of activation function utilized to produce the outcome (ReLu, Sigmoid, etc.).

CapsNet follows a similar process, albeit with vectors instead of tensors:

  • The multiplication of input vectors with a weight matrix.
  • The multiplication of the results with scalar values.
  • The computation of the summation of various vectors with distinct weights.
  • The nonlinearity function utilized was “Squash”.

Let us delve into the mechanics of capsule networks to discover more about this intriguing technology.

Functionality of capsule networks

  1. Utilizing weight matrices to multiply input vectors

    In this scenario, the input vectors incorporate more information than scalars as they comprise of multiple components.

    To commence the process, matrices with predetermined weights are multiplied with the vectors to integrate spatial context information of the data. Normally, such information remains unheeded when implementing convolutional neural networks.

    An overall feature of the image can be estimated through analysis of low-level features. For instance, if the directional orientation of lower-level features such as the eyes, nose, and mouth in the image are in agreement with the shape of the face, then the image can be precisely classified as that of a face.
  2. The procedure of multiplying with scalar weights

    Capsule networks avoid backpropagation and instead employ dynamic routing to determine and modify the weights.

    These weights signal which capsule at a higher level will acquire output from the current capsule.
  3. Calculation of sum of various vectors with distinct weights

    The outcomes derived from the previous procedure are summed up (there is nothing complicated here).
  4. Employing non-linear analysis with the “Squash” function

    To introduce non-linearity into the system, an activation function referred to as “Squash” is utilized. This function preserves the vector’s orientation while scaling it between -1 and 1.

Why has the shift from neural networks to Capsule Networks not occurred yet?

Capsule Networks (CapsNets) have demonstrated improved performance compared to traditional neural networks in several aspects. These advantages include the capacity to preserve spatial information, reduced reliance on extensive training data, quicker training time, and diminished loss of features due to pooling. All of these benefits render CapsNets an attractive option for multiple applications.

Capsules have elevated the accuracy levels for the MNIST dataset, which is deemed relatively simple. However, when utilizing more intricate datasets such as ImageNet and CIFAR-10, capsular performance diminishes under the immense quantity of data. Subsequent research and development is needed to maintain the stability, flexibility, and computational efficiency of capsules, which is typical for emerging technologies.

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