# Local Back-Propagation for Forward-Forward Networks: Independent Unsupervised Layer-Wise Training
## 4 Experiments
### Experimental Results  
Table 3 presents the best performance results obtained from 10 runs of hyperparameter tuning for all experiments, including both sequence and separate training methods. All performances reported are measured in terms of accuracy. Across all experiments, the CNN model trained with BP consistently achieved the highest performance. On all datasets tested, both FF and LBP models, except for CAE-LBP, showed slightly lower performance than the MLP model. However, CAE-LBP consistently outperformed MLP. Additionally, LBP models generally demonstrated better performance than FF. Due to the requirement in FF that the number of input image pixels must match the number of labels, using FF on the  
CIFAR100 dataset, which has 100 labels, would result in a substantial loss of input image information. Therefore, we did not conduct FF experiments on the CIFAR100 dataset. Under the sequence training approach, where each cell was trained for one epoch before moving to the next, FF achieved higher performance than most LBP models except for CAE-LBP. In contrast, when using the separate training approach, where the previous cells were fully trained up to the maximum number of epochs before training the next cell, LBP models generally outperformed their sequence training results and performed better than FF. Notably, when applying separate training on the CIFAR10 dataset, FF experienced a significant decrease in performance. Figures 4 and 5 show box plots of the results from the 10 hyperparameter tuning runs on the MNIST dataset for both sequence and separate training, covering the full range of experimental outcomes. The \(y\)-axis indicates accuracy, and the \(x\)-axis lists the models in order: SLP, MLP, CNN, FF, AE-LBP, DAE-LBP, CAE-LBP, and GAN-LBP. The circular points represent outliers, and the colored line inside each box denotes the median value. Each box extends from the 25 th to the 75 th percentile of the measured values. Figure 4 shows the sequence training results. Except for FF and GAN-LBP, most models exhibit stable performance distributions. Figure 5 shows the separate training results, where FF's performance distribution is notably unstable. Figure 6, which shows the sequence training results for CIFAR10, indicates a broader performance range compared to MNIST. Notably, FF and DAE-LBP display unstable distributions. Figure 7, presenting the separate training results for CIFAR10, similarly shows that FF and DAE-LBP remain unstable. In contrast, GAN-LBP demonstrates more stable performance in separate training on CIFAR10 than it did on MNIST. As shown, the FF model exhibits highly unstable performance across both datasets, especially on CIFAR-10, where its accuracy is both low and inconsistent. This suggests that despite using a similar number of parameters, the structural limitations of FF make it unsuitable for learning from datasets with higher information complexity. In contrast, the proposed LBP variants show relatively stable performance on MNIST. On CIFAR-10, however, some variants display signs of instability.