3 citations · 5 across the 4 of their papers we have counts for
6 papers
FaceCook: Face Generation Based on Linear Scaling Factors
Tianren Wang, Can Peng, Teng Zhang +1
With the excellent disentanglement properties of state-of-the-art generative models, image editing has been the dominant approach to control the attributes of synthesised face imag…
DIODE: Dilatable Incremental Object Detection
Can Peng, Kun Zhao, Sam Maksoud +2
To accommodate rapid changes in the real world, the cognition system of humans is capable of continually learning concepts. On the contrary, conventional deep learning models lack…
Scalable Bayesian Deep Learning with Kernel Seed Networks
Sam Maksoud, Kun Zhao, Can Peng +1
This paper addresses the scalability problem of Bayesian deep neural networks. The performance of deep neural networks is undermined by the fact that these algorithms have poorly c…
SID: Incremental Learning for Anchor-Free Object Detection via Selective and Inter-Related Distillation
Can Peng, Kun Zhao, Sam Maksoud +2
Incremental learning requires a model to continually learn new tasks from streaming data. However, traditional fine-tuning of a well-trained deep neural network on a new task will…
Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN
Can Peng, Kun Zhao, Brian C. Lovell
The human vision and perception system is inherently incremental where new knowledge is continually learned over time whilst existing knowledge is retained. On the other hand, deep…
To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?
Can Peng, Kun Zhao, Arnold Wiliem +4
The condition of the Glomeruli, or filter sacks, in renal Direct Immunofluorescence (DIF) specimens is a critical indicator for diagnosing kidney diseases. A digital pathology syst…