51 citations · 205 across the 19 of their papers we have counts for
11 papers · 1 filter
Supervised Knowledge May Hurt Novel Class Discovery Performance
Ziyun Li, Jona Otholt, Ben Dai +3
Novel class discovery (NCD) aims to infer novel categories in an unlabeled dataset by leveraging prior knowledge of a labeled set comprising disjoint but related classes. Given tha…
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…
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…
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…
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…
K-Metamodes: frequency- and ensemble-based distributed k-modes clustering for security analytics
Andrey Sapegin, Christoph Meinel
Nowadays processing of Big Security Data, such as log messages, is commonly used for intrusion detection purposed. Its heterogeneous nature, as well as combination of numerical and…