标题： 可扩展的前向-前向算法 显示英文标题

标题： Scalable Forward-Forward Algorithm

摘要： 我们提出了一种可扩展的前向-前向（FF）算法，通过单独训练每一层来消除对反向传播的需求。 与反向传播不同， FF 避免了反向梯度，并且可以更加模块化和内存高效，这使其对于大型网络具有吸引力。 我们通过引入一种新的方式计算卷积层的损失，将 FF 扩展到现代卷积架构，如 MobileNetV3 和 ResNet18。 实验表明，我们的方法性能可与标准反向传播相媲美。 此外，当我们把网络分成块，例如 ResNet 中的残差块，并仅在每个块内应用反向传播，而不是跨块进行，我们的混合设计往往在保持类似训练速度的同时优于反向传播基线。 最后，我们在小数据集和迁移学习上进行了实验，证实了我们方法的适应性。 显示英文摘要

摘要： 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.