A recurrent issue in deep learning is the scarcity of data, in particular precisely annotated data. Few publicly available databases are correctly annotated and generating correct labels is very time consuming. The present article investigates into data augmentation strategies for Neural Networks training, particularly for tasks related to drum transcription. These tasks need very precise annotations. This article investigates state-ofthe-art sound transformation algorithms for remixing noise and sinusoidal parts, remixing attacks, transposing with and without time compensation and compares them to basic regularization methods such as using dropout and additive Gaussian noise. And it shows how a drum transcription algorithm based on CNN benefits from the proposed data augmentation strategy.
In recent years, the field of embryo imaging has seen an influx of work using machine learning. These works take advantage of large microscopy datasets collected by fertility clinics as routine practice through relatively standardised imaging setups. Nevertheless, systematic variations still exist between datasets and can harm the ability of machine learning models to perform well across different clinics. In this work, we present Super-Focus, a method for correcting systematic variations present in embryo focal stacks by artificially generating focal planes. We demonstrate that these artificially generated planes are realistic to human experts and that using Super-Focus as a pre-processing step improves the ability of a cell instance segmentation model to generalise across multiple clinics.
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