3 citations · 11 across the 6 of their papers we have counts for
4 papers · 1 filter
Explaining Deep Convolutional Neural Networks via Latent Visual-Semantic Filter Attention
Yu Yang, Seungbae Kim, Jungseock Joo
Interpretability is an important property for visual models as it helps researchers and users understand the internal mechanism of a complex model. However, generating semantic exp…
Explaining AlphaGo: Interpreting Contextual Effects in Neural Networks
Zenan Ling, Haotian Ma, Yu Yang +3
In this paper, we propose to disentangle and interpret contextual effects that are encoded in a pre-trained deep neural network. We use our method to explain the gaming strategy of…
Unsupervised Learning of Neural Networks to Explain Neural Networks
Quanshi Zhang, Yu Yang, Yuchen Liu +2
This paper presents an unsupervised method to learn a neural network, namely an explainer, to interpret a pre-trained convolutional neural network (CNN), i.e., explaining knowledge…
Interpreting CNNs via Decision Trees
Quanshi Zhang, Yu Yang, Haotian Ma +1
This paper aims to quantitatively explain rationales of each prediction that is made by a pre-trained convolutional neural network (CNN). We propose to learn a decision tree, which…