Stuyck T., Demeester E., 2025, Effectiveness of Generative Adversarial Networks in Low Data Availability Environments for the Detection of Chemical Foam, Chemical Engineering Transactions, 115, 73-78.
The chemical sector faces significant challenges in applying deep learning techniques due to the scarcity of extensive and well-labeled datasets. This research investigates the potential of Generative Adversarial Networks (GANs) to address these limitations, exploring scenarios where limited or unlabeled data is available. One approach leverages GAN-generated synthetic data to enhance classification accuracy when dealing with small weakly labeled datasets. This technique has the potential to improve model performance without the extensive data collection and labeling traditionally required. Additionally, a second approach leverages the application of GAN-based anomaly detection algorithms, which offer the ability to identify anomalies in data without the need for manual labeling. This study demonstrates the effectiveness of augmenting small, weakly labeled datasets with synthetic data generated by GANs to significantly improve the classification of chemical foam. It is shown that trustworthy models can be developed that are able to increase the accuracy from 60% up to 91%. Conversely, the research also finds that GAN-based anomaly detection shows less impact in the context of chemical foam detection and segmentation. The method can identify anomalies but fails to completely segment them. These findings offer valuable insights into the potential and limitations of applying GANs for specific tasks within the chemical sector. Keywords: Chemical Engineering; Machine learning; Generative Adversarial Net
