21 citations · 100 across the 35 of their papers we have counts for
5 papers · 1 filter
On the Noise Scheduling for Generating Plausible Designs with Diffusion Models
Jiajie Fan, Laure Vuaille, Thomas Bäck +1
Deep Generative Models (DGMs) are widely used to create innovative designs across multiple industries, ranging from fashion to the automotive sector. In addition to generating imag…
Saliency Can Be All You Need In Contrastive Self-Supervised Learning
Veysel Kocaman, Ofer M. Shir, Thomas Bäck +1
We propose an augmentation policy for Contrastive Self-Supervised Learning (SSL) in the form of an already established Salient Image Segmentation technique entitled Global Contrast…
Preprint: Norm Loss: An efficient yet effective regularization method for deep neural networks
Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck +2
Convolutional neural network training can suffer from diverse issues like exploding or vanishing gradients, scaling-based weight space symmetry and covariant-shift. In order to add…
PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data
Theodoros Georgiou, Sebastian Schmitt, Thomas Bäck +3
Computational Fluid Dynamics (CFD) simulations are a very important tool for many industrial applications, such as aerodynamic optimization of engineering designs like cars shapes,…
Improving Model Accuracy for Imbalanced Image Classification Tasks by Adding a Final Batch Normalization Layer: An Empirical Study
Veysel Kocaman, Ofer M. Shir, Thomas Bäck
Some real-world domains, such as Agriculture and Healthcare, comprise early-stage disease indications whose recording constitutes a rare event, and yet, whose precise detection at…