5 papers
Active Learning for Imbalanced Civil Infrastructure Data
Thomas Frick, Diego Antognini, Mattia Rigotti +3
Aging civil infrastructures are closely monitored by engineers for damage and critical defects. As the manual inspection of such large structures is costly and time-consuming, we a…
Model-Assisted Labeling via Explainability for Visual Inspection of Civil Infrastructures
Klara Janouskova, Mattia Rigotti, Ioana Giurgiu +1
Labeling images for visual segmentation is a time-consuming task which can be costly, particularly in application domains where labels have to be provided by specialized expert ann…
Enabling Reproducibility and Meta-learning Through a Lifelong Database of Experiments (LDE)
Jason Tsay, Andrea Bartezzaghi, Aleke Nolte +1
Artificial Intelligence (AI) development is inherently iterative and experimental. Over the course of normal development, especially with the advent of automated AI, hundreds or th…
Reducing Data Motion to Accelerate the Training of Deep Neural Networks
Sicong Zhuang, Cristiano Malossi, Marc Casas
This paper reduces the cost of DNNs training by decreasing the amount of data movement across heterogeneous architectures composed of several GPUs and multicore CPU devices. In par…
Efficient Image Dataset Classification Difficulty Estimation for Predicting Deep-Learning Accuracy
Florian Scheidegger, Roxana Istrate, Giovanni Mariani +3
In the deep-learning community new algorithms are published at an incredible pace. Therefore, solving an image classification problem for new datasets becomes a challenging task, a…