126 citations · 180 across the 8 of their papers we have counts for
8 papers
On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy
Vignesh Srinivasan, Nils Strodthoff, Jackie Ma +3
There is an increasing number of medical use-cases where classification algorithms based on deep neural networks reach performance levels that are competitive with human medical ex…
Langevin Cooling for Domain Translation
Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek +1
Domain translation is the task of finding correspondence between two domains. Several Deep Neural Network (DNN) models, e.g., CycleGAN and cross-lingual language models, have shown…
Black-Box Decision based Adversarial Attack with Symmetric -stable Distribution
Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller +2
Developing techniques for adversarial attack and defense is an important research field for establishing reliable machine learning and its applications. Many existing methods emplo…
Multi-Kernel Prediction Networks for Denoising of Burst Images
Talmaj Marinč, Vignesh Srinivasan, Serhan Gül +2
In low light or short-exposure photography the image is often corrupted by noise. While longer exposure helps reduce the noise, it can produce blurry results due to the object and…
Learning The Invisible: A Hybrid Deep Learning-Shearlet Framework for Limited Angle Computed Tomography
T. A. Bubba, G. Kutyniok, M. Lassas +4
The high complexity of various inverse problems poses a significant challenge to model-based reconstruction schemes, which in such situations often reach their limits. At the same…
A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery
Arturo Marban, Vignesh Srinivasan, Wojciech Samek +2
Providing force feedback as relevant information in current Robot-Assisted Minimally Invasive Surgery systems constitutes a technological challenge due to the constraints imposed b…
Robustifying Models Against Adversarial Attacks by Langevin Dynamics
Vignesh Srinivasan, Arturo Marban, Klaus-Robert Müller +2
Adversarial attacks on deep learning models have compromised their performance considerably. As remedies, a lot of defense methods were proposed, which however, have been circumven…
Sharing Hash Codes for Multiple Purposes
Wikor Pronobis, Danny Panknin, Johannes Kirschnick +6
Locality sensitive hashing (LSH) is a powerful tool for sublinear-time approximate nearest neighbor search, and a variety of hashing schemes have been proposed for different dissim…