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Hidenori Ide
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Improvement of learning for CNN with ReLU activation by sparse regularization
H Ide, T Kurita
2017 international joint conference on neural networks (IJCNN), 2684-2691, 2017
3632017
Robust pruning for efficient CNNs
H Ide, T Kobayashi, K Watanabe, T Kurita
Pattern Recognition Letters 135, 90-98, 2020
182020
Texture segmentation using Siamese network and hierarchical region merging
R Yamada, H Ide, N Yudistira, T Kurita
2018 24th International Conference on Pattern Recognition (ICPR), 2735-2740, 2018
62018
Convolutional neural network with discriminant criterion for input of each neuron in output layer
H Ide, T Kurita
Neural Information Processing: 25th International Conference, ICONIP 2018 …, 2018
32018
Low level visual feature extraction by learning of multiple tasks for convolutional neural networks
H Ide, T Kurita
2016 International joint conference on neural networks (IJCNN), 3620-3627, 2016
22016
Decomposition of Invariant and Variant Features by Using Convolutional Autoencoder
H Ide, H Fujishige, J Miyao, T Kurita
International Workshop on Frontiers of Computer Vision, 97-111, 2022
2022
Simple ConvNet Based on Bag of MLP-Based Local Descriptors
T Kobayashi, H Ide, K Watanabe
Neural Information Processing: 26th International Conference, ICONIP 2019 …, 2019
2019
CNN における ReLU 活性化関数に対するスパース正則化の適用と分析
井手秀徳, 栗田多喜夫
電子情報通信学会論文誌 D 101 (8), 1110-1119, 2018
2018
Analysis of sparse regularization for ReLU activation function in CNN
H Ide, T Kurita
IEICE Technical Report; IEICE Tech. Rep. 116 (528), 123-128, 2017
2017
Analysis of Sparse Regularization for ReLU Activation Function in CNN
井手秀徳, 栗田多喜夫
電子情報通信学会技術研究報告= IEICE technical report: 信学技報 116 (527 …, 2017
2017
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