Modern machine learning models are able to outperform humans on a variety of non-trivial tasks. However, as the complexity of the models increases, they consume significant amounts of power and still struggle to generalize effectively to unseen data. Local learning, which focuses on updating subsets of a model's parameters at a time, has emerged as a promising technique to address these issues. Recently, a novel local learning algorithm, called Forward-Forward, has received widespread attention due to its innovative approach to learning. Unfortunately, its application has been limited to smaller datasets due to scalability issues. To this end, we propose The Trifecta, a collection of three simple techniques that synergize exceptionally well and drastically improve the Forward-Forward algorithm on deeper networks. Our experiments demonstrate that our models are on par with similarly structured, backpropagation-based models in both training speed and test accuracy on simple datasets. This is achieved by the ability to learn representations that are informative locally, on a layer-by-layer basis, and retain their informativeness when propagated to deeper layers in the architecture. This leads to around 84% accuracy on CIFAR-10, a notable improvement (25%) over the original FF algorithm. These results highlight the potential of Forward-Forward as a genuine competitor to backpropagation and as a promising research avenue.

翻译：现代机器学习模型能够在多种非平凡任务上超越人类表现。然而，随着模型复杂度增加，它们消耗大量计算资源，且仍难以有效泛化至未见数据。局部学习——一种每次仅更新模型参数子集的技术——已成为解决这些问题的重要方向。近期，名为前馈-前馈（Forward-Forward）的新型局部学习算法因其创新的学习方式受到广泛关注。不幸的是，由于可扩展性问题，该算法目前仅能应用于较小规模数据集。为此，我们提出“三要素”（The Trifecta）：三种协同效应极佳且能显著改进深层网络中前馈-前馈算法的简单技术。实验表明，在简单数据集上，我们的模型在训练速度和测试准确率上与采用反向传播的同类结构模型表现相当。这一成果得益于模型能逐层学习具有局部信息量的表征，并在向架构深层传播时保持其信息有效性。最终在CIFAR-10数据集上达到约84%的准确率，较原始前馈-前馈算法提升显著（25%）。这些结果凸显了前馈-前馈作为反向传播真正竞争者及其作为重要研究方向的潜力。