Enhancing Electronic Component Classification with Supervised Contrastive Learning: A Comparative Evaluation

Electronic component classification is crucial in manufacturing, testing, and recycling. This paper evaluates the effectiveness of supervised contrastive learning in enhancing electronic component classification. Traditional and deep learning network-based methods are commonly employed for this task, with convolutional neural networks (CNNs) showing promising results. The paper evaluates supervised contrastive learning that encourages similar instances to have closer representations in the feature space while pushing different instances apart. The results indicate that supervised contrastive learning does not significantly improve accuracy with abundant data but outperforms the standard CNN approach in few-shot scenarios, achieving an accuracy of 81% compared to 33% in the traditional approach. Supervised contrastive learning has the potential to enhance electronic component classification tasks with limited labeled datasets, which are often costly and difficult to obtain.