Search Results for author: Can Peng

Found 6 papers, 1 papers with code

FaceCook: Face Generation Based on Linear Scaling Factors

no code implementations8 Sep 2021 Tianren Wang, Can Peng, Teng Zhang, Brian Lovell

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 images.

Face Generation

DIODE: Dilatable Incremental Object Detection

no code implementations12 Aug 2021 Can Peng, Kun Zhao, Sam Maksoud, Tianren Wang, Brian C. Lovell

In this paper, we aim to alleviate this performance decay on multi-step incremental detection tasks by proposing a dilatable incremental object detector (DIODE).

Incremental Learning Object Detection

Scalable Bayesian Deep Learning with Kernel Seed Networks

no code implementations19 Apr 2021 Sam Maksoud, Kun Zhao, Can Peng, Brian C. Lovell

To address this problem we present a method for performing BDL, namely Kernel Seed Networks (KSN), which does not require a 2-fold increase in the number of parameters.

Faster ILOD: Incremental Learning for Object Detectors based on Faster RCNN

1 code implementation9 Mar 2020 Can Peng, Kun Zhao, Brian C. Lovell

To address this problem, incremental learning methods have been explored which preserve the old knowledge of deep learning models.

Incremental Learning Knowledge Distillation +1

To What Extent Does Downsampling, Compression, and Data Scarcity Impact Renal Image Analysis?

no code implementations22 Sep 2019 Can Peng, Kun Zhao, Arnold Wiliem, Teng Zhang, Peter Hobson, Anthony Jennings, Brian C. Lovell

Critical findings are observed: (1) The best balance between detection accuracy, detection speed and file size is achieved at 8 times downsampling captured with a $40\times$ objective; (2) compression which reduces the file size dramatically, does not necessarily have an adverse effect on overall accuracy; (3) reducing the amount of training data to some extents causes a drop in precision but has a negligible impact on the recall; (4) in most cases, Faster R-CNN achieves the best accuracy in the glomerulus detection task.

Image Compression

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