## 2 Related Work
### DNN-Based Approach  
In recent years, with the successful advancement of machine learning techniques, particularly the deep neural network (DNN), a new paradigm has emerged for many industrial-related studies (e.g., process planning [36], manufacturing cost estimation [15],and parts classification [37]). Due to the growing popularity of DNN, many researchers are attempting to apply it to machining feature recognition. For example, [PERSON]. [14] proposed a 3D convolutional neural network (3D CNN) called FeatureNet for solving feature recognition. This approach has two stages: an unsupervised watershed algorithm segments isolated features from a voxelized CAD model, and then FeatureNet is used to recognize the category of the isolated feature. The authors of [38] also proposed a 3D CNN that is capable of identifying both the type of machining feature and the manufacturing process (milling or cutting). [PERSON]. [39] proposed a YOLO-based machining feature detection system called MFR-Net, which can recognize shapes, dimensions, tolerances, symbols, and texts of machining features. However, this method detects features from 2D images, losing the geometric and topological information of 3D models. Another similar two-stage approach called MsvNet [20] was proposed, which combined the Felzenszwalb segmentation algorithm and multiple sectional view CNN. Following the same idea, [PERSON]. [40] developed an AAG-based and 3D CNN-based hybrid feature recognition system, which integrated a brand-new DNN technique and the traditional graph-based approach. Inspired by single-stage object detection algorithms, [PERSON]. [32] proposed an end-to-end recognizer SsdNet, and its upgraded version RDetNet addressed highly interacting machining feature recognition [18]. Additionally, some researchers are concentrating on CAD representation that is suitable for DNN. [PERSON]. [41] presented a 3D descriptor called improved dexel representation (IDR), which was designed for feeding CAD holistic information to CNN.  
However, it should be pointed out that DNN is a deeper and more modern artificial neural network (ANN) that uses more training data. Some DL-based feature recognition approaches require a large number of labeled samples for training (e.g., the FeatureNet dataset [14] has 144K labeled examples of machining features, the MFCAD dataset [42] has more than 15K, and [PERSON] et al. proposed a point cloud machining feature dataset [19] that has 55K labeled samples). In practice, collecting and annotating large amounts of data is a time-consuming burden for researchers and developers that has become a bottleneck of deep learning applications in machining feature recognition. In addition, the current feature recognition DL models have a large scale of parameters and computational complexity (FeatureNet has 33.94 M parameters and 12.51 G FLOPs, MsvNet has 128.86 M parameters and 7.46 G FLOPs), which places high demands on computer performance. This reason further limits its application in CAPP.  
As aforementioned, the further development of deep learning in the field of machining feature recognition has been restricted by the requirement of large labeled data and computing resources. The paper proposes two main contributions to address the problems encountered in small sample recognition:  
* We propose a semi-supervised learning framework to improve recognition on small samples. MsvNetLite and FeatureNetLite with semi-supervised learning surpassed their counterparts (MsvNet and FeatureNet) in machining feature classification, demonstrating the effectiveness of the proposed approaches.
* We use lightweight compressing techniques to reduce nearly 99% parameters and 98% computational complexity. Both MsvNetLite and FeatureNetLite achieved significant reductions in parameters and FLOPs compared to their counterparts, proving the efficiency of the proposed approaches in model scale and inference speed.