7 citations · 26 across the 17 of their papers we have counts for
9 papers · 1 filter
Zero-shot Concept Bottleneck Models
Shin'ya Yamaguchi, Kosuke Nishida, Daiki Chijiwa +1
Concept bottleneck models (CBMs) are inherently interpretable and intervenable neural network models, which explain their final label prediction by the intermediate prediction of h…
Evaluating Time-Series Training Dataset through Lens of Spectrum in Deep State Space Models
Sekitoshi Kanai, Yasutoshi Ida, Kazuki Adachi +3
This study investigates a method to evaluate time-series datasets in terms of the performance of deep neural networks (DNNs) with state space models (deep SSMs) trained on the data…
Adaptive Random Feature Regularization on Fine-tuning Deep Neural Networks
Shin'ya Yamaguchi, Sekitoshi Kanai, Kazuki Adachi +1
While fine-tuning is a de facto standard method for training deep neural networks, it still suffers from overfitting when using small target datasets. Previous methods improve fine…
Regularizing Neural Networks with Meta-Learning Generative Models
Shin'ya Yamaguchi, Daiki Chijiwa, Sekitoshi Kanai +2
This paper investigates methods for improving generative data augmentation for deep learning. Generative data augmentation leverages the synthetic samples produced by generative mo…
Toward Data Efficient Model Merging between Different Datasets without Performance Degradation
Masanori Yamada, Tomoya Yamashita, Shin'ya Yamaguchi +1
Model merging is attracting attention as a novel method for creating a new model by combining the weights of different trained models. While previous studies reported that model me…
One-vs-the-Rest Loss to Focus on Important Samples in Adversarial Training
Sekitoshi Kanai, Shin'ya Yamaguchi, Masanori Yamada +3
This paper proposes a new loss function for adversarial training. Since adversarial training has difficulties, e.g., necessity of high model capacity, focusing on important data po…
Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks
Daiki Chijiwa, Shin'ya Yamaguchi, Atsutoshi Kumagai +1
Few-shot learning for neural networks (NNs) is an important problem that aims to train NNs with a few data. The main challenge is how to avoid overfitting since over-parameterized…
Transfer Learning with Pre-trained Conditional Generative Models
Shin'ya Yamaguchi, Sekitoshi Kanai, Atsutoshi Kumagai +2
Transfer learning is crucial in training deep neural networks on new target tasks. Current transfer learning methods always assume at least one of (i) source and target task label…
Pruning Randomly Initialized Neural Networks with Iterative Randomization
Daiki Chijiwa, Shin'ya Yamaguchi, Yasutoshi Ida +2
Pruning the weights of randomly initialized neural networks plays an important role in the context of lottery ticket hypothesis. Ramanujan et al. (2020) empirically showed that onl…