74 citations · 448 across the 40 of their papers we have counts for
6 papers · 1 filter
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…
Multi-Task Generative Adversarial Network for Handling Imbalanced Clinical Data
Mina Rezaei, Haojin Yang, Christoph Meinel
We propose a new generative adversarial architecture to mitigate imbalance data problem for the task of medical image semantic segmentation where the majority of pixels belong to a…
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…
Conditional Generative Refinement Adversarial Networks for Unbalanced Medical Image Semantic Segmentation
Mina Rezaei, Haojin Yang, Christoph Meinel
We propose a new generative adversarial architecture to mitigate imbalance data problem in medical image semantic segmentation where the majority of pixels belongs to a healthy reg…
Learning to Train a Binary Neural Network
Joseph Bethge, Haojin Yang, Christian Bartz +1
Convolutional neural networks have achieved astonishing results in different application areas. Various methods which allow us to use these models on mobile and embedded devices ha…
Dropout-GAN: Learning from a Dynamic Ensemble of Discriminators
Gonçalo Mordido, Haojin Yang, Christoph Meinel
We propose to incorporate adversarial dropout in generative multi-adversarial networks, by omitting or dropping out, the feedback of each discriminator in the framework with some p…