7 citations · 22 across the 10 of their papers we have counts for
10 papers
Inductive Graph Few-shot Class Incremental Learning
Yayong Li, Peyman Moghadam, Can Peng +2
Node classification with Graph Neural Networks (GNN) under a fixed set of labels is well known in contrast to Graph Few-Shot Class Incremental Learning (GFSCIL), which involves lea…
Multivariate Prototype Representation for Domain-Generalized Incremental Learning
Can Peng, Piotr Koniusz, Kaiyu Guo +2
Deep learning models suffer from catastrophic forgetting when being fine-tuned with samples of new classes. This issue becomes even more pronounced when faced with the domain shift…
Unified Framework for Histopathology Image Augmentation and Classification via Generative Models
Meng Li, Chaoyi Li, Can Peng +1
Deep learning techniques have become widely utilized in histopathology image classification due to their superior performance. However, this success heavily relies on the availabil…
Few-Shot Class-Incremental Learning from an Open-Set Perspective
Can Peng, Kun Zhao, Tianren Wang +2
The continual appearance of new objects in the visual world poses considerable challenges for current deep learning methods in real-world deployments. The challenge of new task lea…
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…