3 citations · 4 across the 6 of their papers we have counts for
6 papers
Takeuchi's Information Criteria as Generalization Measures for DNNs Close to NTK Regime
Hiroki Naganuma, Taiji Suzuki, Rio Yokota +3
Generalization measures have been studied extensively in the machine learning community to better characterize generalization gaps. However, establishing a reliable generalization…
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…
Empirical Study on Optimizer Selection for Out-of-Distribution Generalization
Hiroki Naganuma, Kartik Ahuja, Shiro Takagi +5
Modern deep learning systems do not generalize well when the test data distribution is slightly different to the training data distribution. While much promising work has been acco…
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…
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…
Pairwise Rotation Hashing for High-dimensional Features
Kohta Ishikawa, Ikuro Sato, Mitsuru Ambai
Binary Hashing is widely used for effective approximate nearest neighbors search. Even though various binary hashing methods have been proposed, very few methods are feasible for e…