Search Results for author: Gabriele Valvano

Found 11 papers, 7 papers with code

Controllable Image Synthesis of Industrial Data Using Stable Diffusion

no code implementations6 Jan 2024 Gabriele Valvano, Antonino Agostino, Giovanni De Magistris, Antonino Graziano, Giacomo Veneri

Training supervised deep neural networks that perform defect detection and segmentation requires large-scale fully-annotated datasets, which can be hard or even impossible to obtain in industrial environments.

Defect Detection Image Generation

Measuring the Biases and Effectiveness of Content-Style Disentanglement

4 code implementations27 Aug 2020 Xiao Liu, Spyridon Thermos, Gabriele Valvano, Agisilaos Chartsias, Alison O'Neil, Sotirios A. Tsaftaris

In this paper, we conduct an empirical study to investigate the role of different biases in content-style disentanglement settings and unveil the relationship between the degree of disentanglement and task performance.

Disentanglement Image-to-Image Translation

Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates

3 code implementations2 Jul 2020 Gabriele Valvano, Andrea Leo, Sotirios A. Tsaftaris

We evaluated our model on several medical (ACDC, LVSC, CHAOS) and non-medical (PPSS) datasets, and we report performance levels matching those achieved by models trained with fully annotated segmentation masks.

Deep Attention Image Segmentation +6

Temporal Consistency Objectives Regularize the Learning of Disentangled Representations

1 code implementation29 Aug 2019 Gabriele Valvano, Agisilaos Chartsias, Andrea Leo, Sotirios A. Tsaftaris

There has been an increasing focus in learning interpretable feature representations, particularly in applications such as medical image analysis that require explainability, whilst relying less on annotated data (since annotations can be tedious and costly).

Anatomy Disentanglement

Unsupervised Data Selection for Supervised Learning

no code implementations29 Oct 2018 Gabriele Valvano, Andrea Leo, Daniele Della Latta, Nicola Martini, Gianmarco Santini, Dante Chiappino, Emiliano Ricciardi

Recent research put a big effort in the development of deep learning architectures and optimizers obtaining impressive results in areas ranging from vision to language processing.

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