Training Convolutional Neural Networks with the Forward-Forward Algorithm

In this article, we explore the use of two deep learning architectures – Forward-Forward (FF) and Linear Classifier – for image classification tasks. FF is a novel approach that utilizes multiple forward passes to improve accuracy, while the Linear Classifier is a simpler method that relies on a single pass through the data.
To better understand these methods, imagine you are trying to solve a complex puzzle with many pieces. The Linear Classifier is like using a single piece of string to connect all the pieces, while FF is like using multiple pieces of string in different colors to create a more detailed and accurate picture.
We tested both approaches on several datasets and found that FF consistently outperformed the Linear Classifier in terms of accuracy, albeit at a higher computational cost. This result suggests that FF may be a better choice when accuracy is paramount, but the computational resources available are limited.
Our findings also revealed that the goodness approach – which involves adjusting the learning rate and number of epochs for each dataset – can further improve the performance of FF. This is like fine-tuning the puzzle pieces to better fit together.
Overall, our study demonstrates that both FF and Linear Classifier have their strengths and weaknesses, and the choice between them depends on the specific needs of the task at hand. By understanding these different approaches, we can make more informed decisions when selecting a deep learning architecture for image classification tasks.