• Journal of IKEEE
• /
• v.26 no.2
• /
• pp.176-185
• /
• 2022

Data augmentation is general approach to solve overfitting of machine learning models by applying various data transformations and distortions to dataset. However, when data augmentation is applied in GAN-based model, which is deep learning image generation model, data transformation and distortion are reflected in the generated image, then the generated image quality decrease. To prevent this problem called augmentation leak, we propose a scheme that can prevent augmentation leak regardless of the type and number of augmentations. Specifically, we analyze the conditions of augmentation leak occurrence by type and implement auxiliary augmentation task classifier that can prevent augmentation leak. Through experiments, we show that the proposed technique prevents augmentation leak in the GAN model, and as a result improves the quality of the generated image. We also demonstrate the superiority of the proposed scheme through ablation study and comparison with other representative augmentation leak prevention technique.

• Archives of Plastic Surgery
• /
• v.39 no.5
• /
• pp.448-451
• /
• 2012

One of the techniques for augmentation mammaplasty is the procedure using implants. Even though this technique has been used for many years, there are still several controversial issues to be discussed and overcome for patient safety. In this review article, capsular contracture, leak or rupture of the implants, possible systemic disease, relation with breast cancer, and recent problems with Poly Implant Prothese implants are described and discussed.