Unlocking the Potential of Convolutional Neural Networks

In the world of neural networks, there's a lot of talk about how to make them better. One big issue is the back-propagation algorithm. It's been around for a while, but it has some problems. It can overfit data, which means it learns the noise instead of the actual pattern. It can also have vanishing or exploding gradients, which mess up the learning process. Plus, it's slow and a bit of a black box, meaning it's hard to understand what's going on inside. So, what's the alternative? Enter the forward-forward network. It's a newer approach that's been gaining attention. But there's a catch. It doesn't always work well with deeper networks, especially convolutional neural networks. That's where the visual forward-forward network, or VFF-Net, comes in. VFF-Net is designed to boost the performance of forward-forward networks in convolutional neural networks. It uses a clever method called label-wise noise labeling. This helps to keep the important information from the input data from getting lost. It also uses something called cosine-similarity-based contrastive loss. This helps to solve the performance drop problem that can happen with the goodness function in convolutional neural networks. But that's not all. VFF-Net also groups layers with the same output channel. This makes it easier to use with existing convolutional neural network models. It reduces the number of minima that need to be optimized, which is a good thing. It also shows the benefits of ensemble training, which is a technique where multiple models work together to improve performance. So, how does VFF-Net stack up against the competition? On a model with four convolutional layers, it improves the test error by up to 8. 31% and 3. 80% on the CIFAR-10 and CIFAR-100 datasets, respectively. That's compared to the forward-forward network model targeting a conventional convolutional neural network. Plus, the fully connected layer-based VFF-Net achieved a test error of 1. 70% on the MNIST dataset. That's better than the existing back-propagation algorithm. In short, VFF-Net is a significant step forward. It reduces the performance gap with back-propagation by improving the forward-forward network. It's also flexible and can be used with existing convolutional neural network-based models. But remember, while VFF-Net shows promise, it's not a magic solution. It's just one piece of the puzzle in the ongoing quest to make neural networks better.