11 citations · 31 across the 9 of their papers we have counts for
8 papers · 1 filter
Knowledge Assembly: Semi-Supervised Multi-Task Learning from Multiple Datasets with Disjoint Labels
Federica Spinola, Philipp Benz, Minhyeong Yu +1
In real-world scenarios we often need to perform multiple tasks simultaneously. Multi-Task Learning (MTL) is an adequate method to do so, but usually requires datasets labeled for…
Adversarial Robustness Comparison of Vision Transformer and MLP-Mixer to CNNs
Philipp Benz, Soomin Ham, Chaoning Zhang +2
Convolutional Neural Networks (CNNs) have become the de facto gold standard in computer vision applications in the past years. Recently, however, new model architectures have been…
ResNet or DenseNet? Introducing Dense Shortcuts to ResNet
Chaoning Zhang, Philipp Benz, Dawit Mureja Argaw +5
ResNet or DenseNet? Nowadays, most deep learning based approaches are implemented with seminal backbone networks, among them the two arguably most famous ones are ResNet and DenseN…
CD-UAP: Class Discriminative Universal Adversarial Perturbation
Chaoning Zhang, Philipp Benz, Tooba Imtiaz +1
A single universal adversarial perturbation (UAP) can be added to all natural images to change most of their predicted class labels. It is of high practical relevance for an attack…
Revisiting Batch Normalization for Improving Corruption Robustness
Philipp Benz, Chaoning Zhang, Adil Karjauv +1
The performance of DNNs trained on clean images has been shown to decrease when the test images have common corruptions. In this work, we interpret corruption robustness as a domai…
Data from Model: Extracting Data from Non-robust and Robust Models
Philipp Benz, Chaoning Zhang, Tooba Imtiaz +1
The essence of deep learning is to exploit data to train a deep neural network (DNN) model. This work explores the reverse process of generating data from a model, attempting to re…