70 citations · 231 across the 35 of their papers we have counts for
10 papers · 1 filter
Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation
Yang He, Shadi Rahimian, Bernt Schiele +1
Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collecti…
Meta-Transfer Learning through Hard Tasks
Qianru Sun, Yaoyao Liu, Zhaozheng Chen +2
Meta-learning has been proposed as a framework to address the challenging few-shot learning setting. The key idea is to leverage a large number of similar few-shot tasks in order t…
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks
David Stutz, Matthias Hein, Bernt Schiele
Adversarial training yields robust models against a specific threat model, e.g., adversarial examples. Typically robustness does not generalize to previously unseen thre…
"Best-of-Many-Samples" Distribution Matching
Apratim Bhattacharyya, Mario Fritz, Bernt Schiele
Generative Adversarial Networks (GANs) can achieve state-of-the-art sample quality in generative modelling tasks but suffer from the mode collapse problem. Variational Autoencoders…
Conditional Flow Variational Autoencoders for Structured Sequence Prediction
Apratim Bhattacharyya, Michael Hanselmann, Mario Fritz +2
Prediction of future states of the environment and interacting agents is a key competence required for autonomous agents to operate successfully in the real world. Prior work for s…
Interpretability Beyond Classification Output: Semantic Bottleneck Networks
Max Losch, Mario Fritz, Bernt Schiele
Today's deep learning systems deliver high performance based on end-to-end training. While they deliver strong performance, these systems are hard to interpret. To address this iss…