254 citations · 285 across the 9 of their papers we have counts for
4 papers · 1 filter
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…
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…
BAGAN: Data Augmentation with Balancing GAN
Giovanni Mariani, Florian Scheidegger, Roxana Istrate +2
Image classification datasets are often imbalanced, characteristic that negatively affects the accuracy of deep-learning classifiers. In this work we propose balancing GAN (BAGAN)…