3 citations · 3 across the 5 of their papers we have counts for
6 papers
Semi-supervised learning made simple with self-supervised clustering
Enrico Fini, Pietro Astolfi, Karteek Alahari +4
Self-supervised learning models have been shown to learn rich visual representations without requiring human annotations. However, in many real-world scenarios, labels are partiall…
Improved baselines for vision-language pre-training
Enrico Fini, Pietro Astolfi, Adriana Romero-Soriano +2
Contrastive learning has emerged as an efficient framework to learn multimodal representations. CLIP, a seminal work in this area, achieved impressive results by training on paired…
Instance-Conditioned GAN Data Augmentation for Representation Learning
Pietro Astolfi, Arantxa Casanova, Jakob Verbeek +3
Data augmentation has become a crucial component to train state-of-the-art visual representation models. However, handcrafting combinations of transformations that lead to improved…
Supervised Tractogram Filtering using Geometric Deep Learning
Pietro Astolfi, Ruben Verhagen, Laurent Petit +5
A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal…
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy
Gabriele Amorosino, Denis Peruzzo, Pietro Astolfi +4
Brains with complex distortion of cerebral anatomy present several challenges to automatic tissue segmentation methods of T1-weighted MR images. First, the very high variability in…
Tractogram filtering of anatomically non-plausible fibers with geometric deep learning
Pietro Astolfi, Ruben Verhagen, Laurent Petit +4
Tractograms are virtual representations of the white matter fibers of the brain. They are of primary interest for tasks like presurgical planning, and investigation of neuroplastic…