activity
20172019
most citedInterpreting CNN Knowledge via an Explanatory Graph

33 citations · 54 across the 5 of their papers we have counts for

collaborators

5 papers

eess.IV2019

Prostate cancer inference via weakly-supervised learning using a large collection of negative MRI

Ruiming Cao, Xinran Zhong, Fabien Scalzo +2

Recent advances in medical imaging techniques have led to significant improvements in the management of prostate cancer (PCa). In particular, multi-parametric MRI (mp-MRI) continue…

cs.CV20182 cited

Explanatory Graphs for CNNs

Quanshi Zhang, Xin Wang, Ruiming Cao +3

This paper introduces a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside conv-layers of a pre-trained CNN. Each filter in a conv-la…

cs.CV20182 cited

Mining Interpretable AOG Representations from Convolutional Networks via Active Question Answering

Quanshi Zhang, Ruiming Cao, Ying Nian Wu +1

In this paper, we present a method to mine object-part patterns from conv-layers of a pre-trained convolutional neural network (CNN). The mined object-part patterns are organized b…

cs.CV201733 cited

Interpreting CNN Knowledge via an Explanatory Graph

Quanshi Zhang, Ruiming Cao, Feng Shi +2

This paper learns a graphical model, namely an explanatory graph, which reveals the knowledge hierarchy hidden inside a pre-trained CNN. Considering that each filter in a conv-laye…

cs.CV201717 cited

Interactively Transferring CNN Patterns for Part Localization

Quanshi Zhang, Ruiming Cao, Shengming Zhang +3

In the scenario of one/multi-shot learning, conventional end-to-end learning strategies without sufficient supervision are usually not powerful enough to learn correct patterns fro…