Transformers vs. Convolutional Neural Networks: What’s the Difference?

If you want to use a deep learning neural network to extract meaningful data from images, you may decide to use a convolutional neural network or a visual transformer. While both algorithms have features that make them well-adapted to computer vision tasks, they approach the problem differently and work in different ways. This means they have distinct advantages and challenges that can affect which model will work best for your task. 

Explore transformers versus convolutional neural networks to learn how each of these models works, what you can use them for, and how they compare to each other. 

Transformers vs. convolutional neural networks

The main difference between a transformer and a convolutional neural network is that they have different architectures that allow them to approach computer vision problems in different ways. First, learn more about transformer models and how they work. Then, explore CNNs and their applications. Finally, explore how these two models compare and contrast to each other. 

What is a transformer model? 

A transformer is a deep learning neural network that uses an encoder/decoder architecture to first break down the input into the base characteristics that define the data, then use its understanding of patterns to rebuild the data with original but similar information. For example, transformer models are commonly used with natural language processing because these algorithms can understand the patterns found in language over a huge range of training materials. When you enter a prompt, the transformer can use this understanding to provide you with unique text that looks like it could have been written by a human by guessing the correct words to use. 

When it comes to computer vision, transformers work in the same way but using the pixels of an image instead of words or phrases to understand the data’s spatial construction and to perform the reasoning you ask it to perform in your prompt. For example, you could ask a transformer model to analyze a medical image to determine if the patient may need treatment. 

How does a vision transformer model work? 

Transformers work by first segmenting the input into tokens, which are small pieces of the data. In a text document, these could be a part of a word, an entire word, or a small phrase. In a vision transformer, the input may include an image, and the transformer uses a similar process to understand it. Next, a transformer uses a self-attention mechanism to determine which parts of the input are most important to inform what the output should look like. This ability to evaluate the data for the most critical components allows a transformer model to be more accurate and to think more rationally about the entire data set. 

What are transformers used for?

Transformers are well-suited for natural language processing, including text generation. The popular large language model ChatGPT uses a transformer (GPT stands for generative pre-trained transformer). You can use them for computer vision, especially for image classification, detection, and segmentation. You can also adapt transformers to a host of other uses, such as real-time language translation and biomedical research, and you can even prevent fraudulent bank transactions. 

What is a convolutional neural network?

A convolutional neural network is a deep learning algorithm used primarily for computer vision tasks such as object detection. Like all neural networks, CNNs pass the input data through a series of layers of individual nodes that can each understand or manipulate the data differently. Convolutional neural networks have layers within their network that allow it to understand how the data relates spatially by moving the data through many hidden layers that pull apart the features of the image and allow the computer to perform the task you ask it to do. 

How does a CNN work?

CNNs are different from other neural networks because their architecture includes convolutional layers, pooling layers, and a fully connected layer within their architecture. The convolutional layer pulls the input apart to understand its features, while the pooling layers downsample the data and reduce the dimensions. The convolutional layer allows the model to understand the important features, while the pooling layer reduces complexity and improves efficiency. 

Next, the model sends the data to the fully connected layer, where each node of the input will match a node to the output. The algorithm maps the results of the convolutional and pooling layers onto the fully connected layer. The output will vary depending on the task you’re performing. For example, in classification, the algorithm would map the output onto probabilities that the object falls into one category or another. 

What are convolutional neural networks used for?

The hidden layers within a convolutional neural network are well-suited for computer vision tasks, such as object detection or classification. Health professionals can also use CNNs for medical imaging to get a more accurate and fast reading of medical images, such as looking for cancer cells in tissue. For technology like self-driving cars, convolutional neural networks can make decisions about what to do based on what they see in front of them. 

Transformers vs. CNNs: Other things to consider

While it may seem that both of these models can accomplish the same task, their different approaches to computer vision mean that they have strengths and weaknesses. Explore how these two models compare to each other in more detail. 

Context: Transformers use self-attention mechanisms to understand all of the pixels at the same time, while CNNs can only consider the surrounding pixels in iterating waves. If you were using these models for natural language processing, a CNN would only consider a sentence or so at a time, while the transformer would consider the entire document. 

Computational resources: A CNN has a smaller, more efficient architecture, which can be more useful in some circumstances compared to the larger, computationally expensive transformer model. The self-attention mechanism in transformers requires a lot of computer power to operate, which can make them more expensive. 

Learn more about deep learning on Coursera

Transformer models and convolutional neural networks are both deep learning models that you can use for computer vision, but they are different in how they empower computer vision. If you want to learn more about deep learning, you can start an online course to help you learn new skills and earn a Professional Certificate you can share with potential employers. For example, Deep Learning Specialization offered by Deep Learning.AI can help you learn more about CNNs, transformer models, recurrent neural networks, and more.