107 citations · 123 across the 13 of their papers we have counts for
5 papers · 1 filter
Fixed-Weight Difference Target Propagation
Tatsukichi Shibuya, Nakamasa Inoue, Rei Kawakami +1
Target Propagation (TP) is a biologically more plausible algorithm than the error backpropagation (BP) to train deep networks, and improving practicality of TP is an open issue. TP…
Informative Sample-Aware Proxy for Deep Metric Learning
Aoyu Li, Ikuro Sato, Kohta Ishikawa +2
Among various supervised deep metric learning methods proxy-based approaches have achieved high retrieval accuracies. Proxies, which are class-representative points in an embedding…
PoF: Post-Training of Feature Extractor for Improving Generalization
Ikuro Sato, Ryota Yamada, Masayuki Tanaka +2
It has been intensively investigated that the local shape, especially flatness, of the loss landscape near a minimum plays an important role for generalization of deep models. We d…
Feature Space Particle Inference for Neural Network Ensembles
Shingo Yashima, Teppei Suzuki, Kohta Ishikawa +2
Ensembles of deep neural networks demonstrate improved performance over single models. For enhancing the diversity of ensemble members while keeping their performance, particle-bas…
Implicit Neural Representations for Variable Length Human Motion Generation
Pablo Cervantes, Yusuke Sekikawa, Ikuro Sato +1
We propose an action-conditional human motion generation method using variational implicit neural representations (INR). The variational formalism enables action-conditional distri…