The paper proposes a new algorithm called SymBa that aims to achieve more
biologically plausible learning than Back-Propagation (BP). The algorithm is
based on the Forward-Forward (FF) algorithm, which is a BP-free method for
training neural networks. SymBa improves the FF algorithm's convergence
behavior by addressing the problem of asymmetric gradients caused by
conflicting converging directions for positive and negative samples. The
algorithm balances positive and negative losses to enhance performance and
convergence speed. Furthermore, it modifies the FF algorithm by adding
Intrinsic Class Pattern (ICP) containing class information to prevent the loss
of class information during training. The proposed algorithm has the potential
to improve our understanding of how the brain learns and processes information
and to develop more effective and efficient artificial intelligence systems.
The paper presents experimental results that demonstrate the effectiveness of
SymBa algorithm compared to the FF algorithm and BP.

本文提出了 SymBa 算法，该算法旨在实现比反向传播（BP）更生物学上合理的学习。 SymBa 算法通过解决正负样本收敛方向冲突引起的不对称梯度问题，平衡正负损失以增强性能和收敛速度，并通过添加包含类信息的内在类模式（ICP）来修改 FF 算法、防止推导过程中丢失类信息。实验证明 SymBa 算法相对于 FF 算法和 BP 的有效性。