30 citations · 50 across the 9 of their papers we have counts for
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cs.CV2023★ 3 cited
LatentAugment: Data Augmentation via Guided Manipulation of GAN's Latent Space
Lorenzo Tronchin, Minh H. Vu, Paolo Soda +1
Data Augmentation (DA) is a technique to increase the quantity and diversity of the training data, and by that alleviate overfitting and improve generalisation. However, standard D…
eess.IV2023
A comparative study between paired and unpaired Image Quality Assessment in Low-Dose CT Denoising
Francesco Di Feola, Lorenzo Tronchin, Paolo Soda
The current deep learning approaches for low-dose CT denoising can be divided into paired and unpaired methods. The former involves the use of well-paired datasets, whilst the latt…