## 4 Experimental Results
### Effectiveness of the Proposed Semi-Supervised Learning  
We performed ablation experiments of the proposed semi-supervised learning framework over FeatureNetLite and MsvNetLite to demonstrate its improvements. The training settings and strategies of MsvNet and MsvNetLite were the same as those proposed by [20]. FeatureNet and FeatureNetLite also followed the configuration recommended by [14]. Detailed training settings are as follows:  
* MsvNetLite, FeatureNetLite, FeatureNet, Baseline1, Baseline2, and VoxNet underwent 100 epoch training. The MsvNet was trained with 120 epochs. This is because [PERSON] et al. adopted a two-stage training strategy, initially training the network 20 epochs on the single view of a voxel model. * MsvNetLite and FeatureNetLite using semi-supervised learning were trained by an SGDM optimizer with \(1\times 10^{-6}\) weight decay. MsvNet, FeatureNet, Baseline1, Baseline2, and VoxNet were trained by an Adam optimizer [58] without weight decay. Figure 8: Machining features in the FeatureNet dataset [14]. * The mini-batch size was set to 64. The batch size is limited to the number of training data if the number of training data is less than the batch size. * The initial learning values of MsvNetLite and FeatureNetLite using semi-supervised learning were set to \(1\times 10^{-3}\) for the backbone and 30.0 for the classifier, and a cosine learning rate decay scheduler was used. MsvNet, FeatureNet, Baseline1, Baseline2, and VoxNet were trained with an initial learning rate set to \(1\times 10^{-3}\) and no decay scheduler. * According to [20], the following data augmentation strategies were also adopted in our experiments: Random Rotation, Random Scale, and Random PadCrop. * For fair comparison with [20], the number of sectional views was set to 12 for multi-view approaches. * FeatureNetLite using semi-supervised learning was trained 50 epochs in the self-supervised stage, and MsvNetLite using semi-supervised learning was trained 100 epochs in the self-supervised stage. To examine the generalization performance of the proposed framework on small labeled samples, we evaluated several approaches on the different numbers of labeled data per class for training. For example, when the number of labeled data per class is set to 1, only 24 labeled samples (24 classes \(\times\) 1 sample per class) are used to train the network. Detailed hyper-parameter settings about semi-supervised learning are discussed in Section 4.5. The comparative results are shown in Table 3: \(PT\) denotes initializing the 2D CNN backbone with ImageNet pre-trained weights provided by torchvision and [50], \(DA\) denotes adopting Random Rotation, Random Scale, and Random PadCrop data augmentation during supervised training, and Semi-Sup. denotes applying semi-supervised learning framework on the approach. In the result table, the bold font presents the best accuracy for the test set for each column. It is observed from this table that MsvNetLite has significantly improved accuracy on small amounts of labeled data (\(\leq\)16) with the application of semi-supervised learning. In particular, with just one training sample per class, MsvNetLite performed about 24% better with semi-supervised learning than without semi-supervised learning. Even when compared to the huge network MsvNet, this represents a 19% improvement. However, we can observe that MsvNetLite with semi-supervised learning does not improve performance  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Approach**} & \multirow{2}{*}{ \begin{tabular}{c} **Data** \\ **Format** \\ \end{tabular}  } & \multicolumn{6}{c}{**Training**} & \multicolumn{6}{c}{**Number of**} \\  & & \multicolumn{6}{c}{**Strategy**} & \multicolumn{6}{c}{**Training Data per Class**} \\ \cline{3-11}  & & **FT** & **DA** & **Semi-Sup.