ContrastCAD: Contrastive Learning-based Representation Learning for Computer-Aided Design Models

The success of Transformer-based models has encouraged many researchers to learn CAD models using sequence-based approaches. However, learning CAD models is still a challenge, because they can be represented as complex shapes with long construction sequences. Furthermore, the same CAD model can be expressed using different CAD construction sequences. We propose a novel contrastive learning-based approach, named ContrastCAD, that effectively captures semantic information within the construction sequences of the CAD model. ContrastCAD generates augmented views using dropout techniques without altering the shape of the CAD model. We also propose a new CAD data augmentation method, called a Random Replace and Extrude (RRE) method, to enhance the learning performance of the model when training an imbalanced training CAD dataset. Experimental results show that the proposed RRE augmentation method significantly enhances the learning performance of Transformer-based autoencoders, even for complex CAD models having very long construction sequences. The proposed ContrastCAD model is shown to be robust to permutation changes of construction sequences and performs better representation learning by generating representation spaces where similar CAD models are more closely clustered. Our codes are available at https://github.com/cm8908/ContrastCAD.

A Lightweight CNN-Transformer Model for Learning Traveling Salesman Problems

Transformer-based models show state-of-the-art performance even for large-scale Traveling Salesman Problems (TSPs). However, they are based on fully-connected attention models and suffer from large computational complexity and GPU memory usage. We propose a lightweight CNN-Transformer model based on a CNN embedding layer and partial self-attention. Our CNN-Transformer model is able to better learn spatial features from input data using a CNN embedding layer compared with the standard Transformer models. It also removes considerable redundancy in fully connected attention models using the proposed partial self-attention. Experiments show that the proposed model outperforms other state-of-the-art Transformer-based models in terms of TSP solution quality, GPU memory usage, and inference time. Our model consumes approximately 20% less GPU memory usage and has 45% faster inference time compared with other state-of-the-art Transformer-based models. Our code is publicly available at https://github.com/cm8908/CNN_Transformer3