51 citations · 194 across the 14 of their papers we have counts for
23 papers
Empirical Evaluation of Post-Training Quantization Methods for Language Tasks
Ting Hu, Christoph Meinel, Haojin Yang
Transformer-based architectures like BERT have achieved great success in a wide range of Natural Language tasks. Despite their decent performance, the models still have numerous pa…
BoolNet: Minimizing The Energy Consumption of Binary Neural Networks
Nianhui Guo, Joseph Bethge, Haojin Yang +4
Recent works on Binary Neural Networks (BNNs) have made promising progress in narrowing the accuracy gap of BNNs to their 32-bit counterparts. However, the accuracy gains are often…
AsymmNet: Towards ultralight convolution neural networks using asymmetrical bottlenecks
Haojin Yang, Zhen Shen, Yucheng Zhao
Deep convolutional neural networks (CNN) have achieved astonishing results in a large variety of applications. However, using these models on mobile or embedded devices is difficul…
Evaluating Post-Training Compression in GANs using Locality-Sensitive Hashing
Gonçalo Mordido, Haojin Yang, Christoph Meinel
The analysis of the compression effects in generative adversarial networks (GANs) after training, i.e. without any fine-tuning, remains an unstudied, albeit important, topic with t…
One Model to Reconstruct Them All: A Novel Way to Use the Stochastic Noise in StyleGAN
Christian Bartz, Joseph Bethge, Haojin Yang +1
Generative Adversarial Networks (GANs) have achieved state-of-the-art performance for several image generation and manipulation tasks. Different works have improved the limited und…
microbatchGAN: Stimulating Diversity with Multi-Adversarial Discrimination
Gonçalo Mordido, Haojin Yang, Christoph Meinel
We propose to tackle the mode collapse problem in generative adversarial networks (GANs) by using multiple discriminators and assigning a different portion of each minibatch, calle…