# Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training
## 4 Experiments
### Experimental Results
In particular, DAE-LBP  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & **Training** & \multirow{2}{*}{**Layers**} & \multicolumn{3}{c}{**Baseline**} & \multicolumn{3}{c}{**Ours**} \\  & **Strategy** & & **SLP** & **MLP** & **CNN** & **FF** & **AE-LBP** & **DAE-LBP** & **CAE-LBP** & **GAN-LBP** \\ \hline \multirow{8}{*}{**MNIST**} & \multirow{4}{*}{**Sequence**} & 2 & **0.9875** & 0.9921 & 0.9782 & 0.9689 & 0.9723 & 0.9791 & 0.9569 \\  & & 3 & 0.9856 & 0.9942 & **0.9795** & 0.9706 & 0.9736 & 0.9821 & **0.966** \\  & & 4 & **0.9875** & 0.9944 & 0.9751 & 0.9704 & 0.9722 & 0.9808 & 0.9581 \\  & & 5 & 0.9875 & 0.9951 & 0.9789 & **0.9713** & **0.9737** & 0.9793 & 0.9586 \\  & & 2 & **0.9875** & 0.9921 & 0.9687 & 0.9707 & 0.9697 & 0.9828 & **0.9621** \\  & & 3 & 0.9856 & 0.9942 & 0.9674 & 0.9704 & 0.9710 & 0.9813 & 0.9604 \\  & & 4 & 0.9875 & 0.9944 & 0.9694 & **0.9731** & 0.9721 & 0.9801 & 0.9598 \\  & & 5 & 0.9870 & **0.9951** & **0.9709** & 0.9717 & **0.9729** & 0.9824 & 0.9598 \\ \hline \multirow{8}{*}{**CIFAR10**} & \multirow{4}{*}{**Sequence**} & 2 & **0.5439** & 0.6979 & **0.4886** & 0.4705 & **0.4691** & 0.5704 & 0.4467 \\  & & 3 & 0.5393 & 0.7645 & 0.4713 & **0.4751** & 0.4474 & **0.5726** & 0.4394 \\  & & 4 & 0.5364 & 0.7598 & 0.4751 & 0.4745 & 0.4573 & 0.5219 & **0.4542** \\  & & 5 & 0.5205 & 0.7777 & 0.462 & 0.473 & 0.4535 & 0.5394 & 0.4436 \\  & & 2 & **0.5439** & 0.6979 & 0.3308 & 0.4658 & 0.4539 & 0.5670 & 0.4420 and GAN-LBP tend to be more sensitive and perform worse, which may indicate that they are more vulnerable to noise. Additionally, we observe that the separate configuration shows more stable trends compared to the sequence setting. This suggests that when each cell is trained independently, the stability of earlier cells is crucial for the successful training of subsequent layers. Figure 4: Performance of sequence training on the MNIST dataset. The \(x\)-axis represents models and the \(y\)-axis represents accuracy. The \(x\)-axis displays SLP, MLP, CNN, FF, AE-LBP, DAE-LBP, CAE-LBP, and GAN-LBP. Models using backpropagation (SLP, MLP, CNN) are shown in red, the FF model is shown in green, and our proposed models (AE-LBP, DAE-LBP, CAE-LBP, GAN-LBP) are shown in blue. Figure 5: Performance of separate training on the MNIST dataset. The \(x\)-axis represents models and the \(y\)-axis represents accuracy. The \(x\)-axis displays SLP, MLP, CNN, FF, AE-LBP, DAE-LBP, CAE-LBP, and GAN-LBP. Models using backpropagation (SLP, MLP, CNN) are shown in red, the FF model is shown in green, and our proposed models (AE-LBP, DAE-LBP, CAE-LBP, GAN-LBP) are shown in blue. Nevertheless, our method also shows a general decrease in performance as the task complexity increases. This indicates that further improvement is needed to enhance the capacity of the model to capture and propagate richer representations across layers, especially for more challenging datasets such as CIFAR-10. Figure 6: Performance of sequence training on the CIFAR10 dataset. The \(x\)-axis represents models and the \(y\)-axis represents accuracy. The \(x\)-axis displays SLP, MLP, CNN, FF, AE-LBP, DAE-LBP, CAE-LBP, and GAN-LBP. Models using backpropagation (SLP, MLP, CNN) are shown in red, the FF model is shown in green, and our proposed models (AE-LBP, DAE-LBP, CAE-LBP, GAN-LBP) are shown in blue. Figure 7: Performance of separate training on the CIFAR10 dataset. The \(x\)-axis represents models and the \(y\)-axis represents accuracy. The \(x\)-axis displays SLP, MLP, CNN, FF, AE-LBP, DAE-LBP, CAE-LBP, and GAN-LBP. Models using backpropagation (SLP, MLP, CNN) are shown in red, the FF model is shown in green, and our proposed models (AE-LBP, DAE-LBP, CAE-LBP, GAN-LBP) are shown in blue. The performance on the CIFAR100 datasets can be found in Appendix A.