We propose a scalable Forward-Forward (FF) algorithm that eliminates the need for backpropagation by training each layer separately. Unlike backpropagation, FF avoids backward gradients and can be more modular and memory efficient, making it appealing for large networks. We extend FF to modern convolutional architectures, such as MobileNetV3 and ResNet18, by introducing a new way to compute losses for convolutional layers. Experiments show that our method achieves performance comparable to standard backpropagation. Furthermore, when we divide the network into blocks, such as the residual blocks in ResNet, and apply backpropagation only within each block, but not across blocks, our hybrid design tends to outperform backpropagation baselines while maintaining a similar training speed. Finally, we present experiments on small datasets and transfer learning that confirm the adaptability of our method.

翻译：我们提出了一种可扩展的前向-前向算法，该算法通过逐层独立训练消除了对反向传播的需求。与反向传播不同，前向-前向算法避免了反向梯度计算，具有更强的模块化特性和更高的内存效率，因此适用于大型网络。我们通过引入一种新的卷积层损失计算方法，将前向-前向算法扩展到现代卷积架构（如MobileNetV3和ResNet18）。实验表明，我们的方法取得了与标准反向传播相当的性能。此外，当我们将网络划分为多个模块（如ResNet中的残差块），并在每个模块内部使用反向传播而模块间不使用反向传播时，这种混合设计在保持相近训练速度的同时，往往能超越反向传播基线。最后，我们在小数据集和迁移学习上的实验验证了该方法的适应性。