Mitigating Paucity of Data in Sinusoid Characterization Using Generative Synthetic Noise
Although the remarkable breakthrough offered by Deep Learning (DL) models is numerous computer vision tasks, the need to acquire large amounts of high-quality natural data and fine-grained annotations is a shortcoming that fundamentally increases the cost and time devoted to training these models in real-world applications. Hence, synthetic datasets are considered reliable alternatives that can reduce the data acquisition by replacing or merging with natural data or effective pre-training of the models. To this end, in this work, we propose a novel approach to integrate structural data structures with the synthetic noise structures learned by unsupervised models that mimic the noise structures in natural data. Based on the proposed approach, we introduce the Sinusoid Feature Recognition (SFR) dataset, which contains hard-to-detect fixed-period sinusoid waves. While the previous works in this regard use generative models to sample synthetic data to inflate the training set, we instead apply unsupervised learning models to generate deep synthetic noise which makes training models in the proposed dataset more challenging. We evaluate the segmentation, image reconstruction, and sinusoid characterization models pre-trained or fully trained on the synthetic SFR dataset on a private dataset of grayscale Acoustic Tele-Viewer (ATV) images. Experimental results show that supervision on our proposed synthetic dataset can improve the accuracy of the models by 3-4% via pre-training, and by 17-27% via ad-hoc training while dealing with challenging, realistic real-world images.