Search Results for author: Vignesh Srinivasan

Found 7 papers, 2 papers with code

On the Robustness of Pretraining and Self-Supervision for a Deep Learning-based Analysis of Diabetic Retinopathy

no code implementations25 Jun 2021 Vignesh Srinivasan, Nils Strodthoff, Jackie Ma, Alexander Binder, Klaus-Robert Müller, Wojciech Samek

Our results indicate that models initialized from ImageNet pretraining report a significant increase in performance, generalization and robustness to image distortions.

Contrastive Learning Diabetic Retinopathy Grading

Langevin Cooling for Domain Translation

1 code implementation31 Aug 2020 Vignesh Srinivasan, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima

Domain translation is the task of finding correspondence between two domains.

Translation

Black-Box Decision based Adversarial Attack with Symmetric $α$-stable Distribution

no code implementations11 Apr 2019 Vignesh Srinivasan, Ercan E. Kuruoglu, Klaus-Robert Müller, Wojciech Samek, Shinichi Nakajima

Many existing methods employ Gaussian random variables for exploring the data space to find the most adversarial (for attacking) or least adversarial (for defense) point.

Adversarial Attack

Multi-Kernel Prediction Networks for Denoising of Burst Images

2 code implementations5 Feb 2019 Talmaj Marinč, Vignesh Srinivasan, Serhan Gül, Cornelius Hellge, Wojciech Samek

The advantages of our method are two fold: (a) the different sized kernels help in extracting different information from the image which results in better reconstruction and (b) kernel fusion assures retaining of the extracted information while maintaining computational efficiency.

Computational Efficiency Image Denoising

A Recurrent Convolutional Neural Network Approach for Sensorless Force Estimation in Robotic Surgery

no code implementations22 May 2018 Arturo Marban, Vignesh Srinivasan, Wojciech Samek, Josep Fernández, Alicia Casals

The results suggest that the force estimation quality is better when both, the tool data and video sequences, are processed by the neural network model.

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