253 citations · 310 across the 24 of their papers we have counts for
5 papers · 1 filter
New Perspective of Interpretability of Deep Neural Networks
Masanari Kimura, Masayuki Tanaka
Deep neural networks (DNNs) are known as black-box models. In other words, it is difficult to interpret the internal state of the model. Improving the interpretability of DNNs is o…
Breaking Inter-Layer Co-Adaptation by Classifier Anonymization
Ikuro Sato, Kohta Ishikawa, Guoqing Liu +1
This study addresses an issue of co-adaptation between a feature extractor and a classifier in a neural network. A naive joint optimization of a feature extractor and a classifier…
Intentional Attention Mask Transformation for Robust CNN Classification
Masanari Kimura, Masayuki Tanaka
Convolutional Neural Networks have achieved impressive results in various tasks, but interpreting the internal mechanism is a challenging problem. To tackle this problem, we exploi…
Interpretation of Feature Space using Multi-Channel Attentional Sub-Networks
Masanari Kimura, Masayuki Tanaka
Convolutional Neural Networks have achieved impressive results in various tasks, but interpreting the internal mechanism is a challenging problem. To tackle this problem, we exploi…
Automatic Labeled LiDAR Data Generation based on Precise Human Model
Wonjik Kim, Masayuki Tanaka, Masatoshi Okutomi +1
Following improvements in deep neural networks, state-of-the-art networks have been proposed for human recognition using point clouds captured by LiDAR. However, the performance of…