254 citations · 285 across the 9 of their papers we have counts for
10 papers
Counterfactual Image Generation for adversarially robust and interpretable Classifiers
Rafael Bischof, Florian Scheidegger, Michael A. Kraus +1
Neural Image Classifiers are effective but inherently hard to interpret and susceptible to adversarial attacks. Solutions to both problems exist, among others, in the form of count…
MAEDAY: MAE for few and zero shot AnomalY-Detection
Eli Schwartz, Assaf Arbelle, Leonid Karlinsky +4
We propose using Masked Auto-Encoder (MAE), a transformer model self-supervisedly trained on image inpainting, for anomaly detection (AD). Assuming anomalous regions are harder to…
Generating Efficient DNN-Ensembles with Evolutionary Computation
Marc Ortiz, Florian Scheidegger, Marc Casas +2
In this work, we leverage ensemble learning as a tool for the creation of faster, smaller, and more accurate deep learning models. We demonstrate that we can jointly optimize for a…
Constrained deep neural network architecture search for IoT devices accounting hardware calibration
Florian Scheidegger, Luca Benini, Costas Bekas +1
Deep neural networks achieve outstanding results in challenging image classification tasks. However, the design of network topologies is a complex task and the research community m…
PAGAN: Portfolio Analysis with Generative Adversarial Networks
Giovanni Mariani, Yada Zhu, Jianbo Li +4
Since decades, the data science community tries to propose prediction models of financial time series. Yet, driven by the rapid development of information technology and machine in…
NeuNetS: An Automated Synthesis Engine for Neural Network Design
Atin Sood, Benjamin Elder, Benjamin Herta +17
Application of neural networks to a vast variety of practical applications is transforming the way AI is applied in practice. Pre-trained neural network models available through AP…