44 citations · 51 across the 2 of their papers we have counts for
5 papers
MeliusNet: Can Binary Neural Networks Achieve MobileNet-level Accuracy?
Joseph Bethge, Christian Bartz, Haojin Yang +2
Binary Neural Networks (BNNs) are neural networks which use binary weights and activations instead of the typical 32-bit floating point values. They have reduced model sizes and al…
KISS: Keeping It Simple for Scene Text Recognition
Christian Bartz, Joseph Bethge, Haojin Yang +1
Over the past few years, several new methods for scene text recognition have been proposed. Most of these methods propose novel building blocks for neural networks. These novel bui…
Back to Simplicity: How to Train Accurate BNNs from Scratch?
Joseph Bethge, Haojin Yang, Marvin Bornstein +1
Binary Neural Networks (BNNs) show promising progress in reducing computational and memory costs but suffer from substantial accuracy degradation compared to their real-valued coun…
Training Competitive Binary Neural Networks from Scratch
Joseph Bethge, Marvin Bornstein, Adrian Loy +2
Convolutional neural networks have achieved astonishing results in different application areas. Various methods that allow us to use these models on mobile and embedded devices hav…
LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks
Christian Bartz, Haojin Yang, Joseph Bethge +1
Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availabil…