284 citations · 405 across the 24 of their papers we have counts for
9 papers · 1 filter
Topological Map Extraction from Overhead Images
Zuoyue Li, Jan Dirk Wegner, Aurélien Lucchi
We propose a new approach, named PolyMapper, to circumvent the conventional pixel-wise segmentation of (aerial) images and predict objects in a vector representation directly. Poly…
A domain agnostic measure for monitoring and evaluating GANs
Paulina Grnarova, Kfir Y Levy, Aurelien Lucchi +4
Generative Adversarial Networks (GANs) have shown remarkable results in modeling complex distributions, but their evaluation remains an unsettled issue. Evaluations are essential f…
Continuous-time Models for Stochastic Optimization Algorithms
Antonio Orvieto, Aurelien Lucchi
We propose new continuous-time formulations for first-order stochastic optimization algorithms such as mini-batch gradient descent and variance-reduced methods. We exploit these co…
Cosmological constraints from noisy convergence maps through deep learning
Janis Fluri, Tomasz Kacprzak, Aurelien Lucchi +3
Deep learning is a powerful analysis technique that has recently been proposed as a method to constrain cosmological parameters from weak lensing mass maps. Due to its ability to l…
A Distributed Second-Order Algorithm You Can Trust
Celestine Dünner, Aurelien Lucchi, Matilde Gargiani +3
Due to the rapid growth of data and computational resources, distributed optimization has become an active research area in recent years. While first-order methods seem to dominate…
Adversarially Robust Training through Structured Gradient Regularization
Kevin Roth, Aurelien Lucchi, Sebastian Nowozin +1
We propose a novel data-dependent structured gradient regularizer to increase the robustness of neural networks vis-a-vis adversarial perturbations. Our regularizer can be derived…