128 citations · 132 across the 3 of their papers we have counts for
5 papers · 1 filter
Enhancing Traffic Scene Predictions with Generative Adversarial Networks
Peter König, Sandra Aigner, Marco Körner
We present a new two-stage pipeline for predicting frames of traffic scenes where relevant objects can still reliably be detected. Using a recent video prediction network, we first…
Further advantages of data augmentation on convolutional neural networks
Alex Hernández-García, Peter König
Data augmentation is a popular technique largely used to enhance the training of convolutional neural networks. Although many of its benefits are well known by deep learning resear…
Learning robust visual representations using data augmentation invariance
Alex Hernández-García, Peter König, Tim C. Kietzmann
Deep convolutional neural networks trained for image object categorization have shown remarkable similarities with representations found across the primate ventral visual stream. Y…
Data augmentation instead of explicit regularization
Alex Hernández-García, Peter König
Contrary to most machine learning models, modern deep artificial neural networks typically include multiple components that contribute to regularization. Despite the fact that some…
Do deep nets really need weight decay and dropout?
Alex Hernández-García, Peter König
The impressive success of modern deep neural networks on computer vision tasks has been achieved through models of very large capacity compared to the number of available training…