When it comes to choosing between RNN vs CNN, the right neural network will depend on the type of data you have and the outputs that you require. While RNNs (recurrent neural networks) are majorly used for text classification, CNNs (convolutional neural networks) help in image identification and classification. There are a lot of differences between the two, but that does not mean they are mutually exclusive. It’s also possible for you to use both RNNs and CNNs together in order to leverage their benefits. In this article, we’ll discuss what is the difference between RNN and CNN, and when to use each one.
RNN vs CNN: Understanding the Difference
Let’s understand each neural network individually in detail.
What is RNN?
RNN or recurrent neural network is a class of artificial neural networks that processes information sequences like temperatures, daily stock prices, and sentences. These algorithms are designed to take a series of inputs without any predetermined size limit. More importantly, what makes RNN unique is that these algorithms process sequences by retaining the memory of the previous value or state in the sequence. So, in RNNs the output of the current step becomes the input of the next step and so on. This means, at every stage, the model considers both the current input and all of the previous outputs.
The many applications of RNNs include:
- Speech recognition
- Time series prediction
- Music composition
- Machine translation
What is CNN?
CNNs or convolutional neural networks are a category of neural networks that are majorly used for image classification and recognition. CNNs have been proven to be successful in identifying objects, signs, and even faces. These deep learning algorithms take an image as the input to detect and assign importance to the various features of the image in order to differentiate one image from the other.
While simple neural networks have some success in classifying basic binary images, they can’t handle complex images with pixel dependencies. They also don’t have the computational power which is needed to handle images with large pixels, which is exactly where CNNs come in. CNN helps in classifying even the most complex of images with high accuracy. CNN algorithms can also apply relevant filters to identify spatial as well as temporal dependencies in images.
Some of the many applications of CNN are
- Facial recognition
- Analysing documents
- Understanding climate patterns
- Video classification
RNN or CNN: Which one is Better?
1. Type of input data
While RNNs are suitable for handling temporal or sequential data, CNNs are suitable for handling spatial data (images). Though both models work a bit similarly by introducing sparsity and reusing the same neurons and weights over time (in case of RNN) or over different parts of the image (in case of CNN).
2. Computing power
Since both RNN and CNN are used for different purposes, it might not be appropriate to compare their computational ability. Though if we had to, CNN would be more powerful than RNN. That’s mainly because RNN has less feature compatibility and it has the ability to take arbitrary output/input lengths which can affect the total computational time and efficiency. On the other hand, CNN takes fixed input and gives a fixed output which allows it to compute the results at a faster pace.
Convolutional neural networks use the connectivity patterns available in neurons. Inspired by the visual cortex of the brain, CNNs have numerous layers and each one is responsible for detecting a specific set of features in the image. The combined output of all the layers helps CNNs identify and classify images.
Recurrent neural networks use time-series information to identify patterns between the input and output. The memory of RNN algorithms allows them to learn more about long-term dependencies in data and understand the whole context of the sequence while making the next prediction.
Leveraging the Power of RNN-CNN Hybrids
While RNNs and CNNs have several differences, they are not completely mutually exclusive. It is actually possible for you to use them together for increased effectiveness. This can especially be helpful when the input has to be classified as visually complex with temporal characteristics. Since CNN can only handle spatial data, you will have to use RNN to handle the temporal data.
As you can see, there is no clear winner when it comes to RNN vs CNN. The right neural network will depend on your project requirements and the type of input data you already have. When these two networks are combined, the resultant network is also known as CRNN. In a combined network, the input is first passed through the CNN layers and then its output is fed to the RNN network layer. These hybrid structures are being currently used for applications like gesture recognition, video scene labelling, video identification, and DNA sequencing.
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