7 citations · 26 across the 17 of their papers we have counts for
17 papers
Rationale-Enhanced Decoding for Multi-modal Chain-of-Thought
Shin'ya Yamaguchi, Kosuke Nishida, Daiki Chijiwa
Large vision-language models (LVLMs) have demonstrated remarkable capabilities by integrating pre-trained vision encoders with large language models (LLMs). Similar to single-modal…
Post-pre-training for Modality Alignment in Vision-Language Foundation Models
Shin'ya Yamaguchi, Dewei Feng, Sekitoshi Kanai +2
Contrastive language image pre-training (CLIP) is an essential component of building modern vision-language foundation models. While CLIP demonstrates remarkable zero-shot performa…
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…
Adversarial Finetuning with Latent Representation Constraint to Mitigate Accuracy-Robustness Tradeoff
Satoshi Suzuki, Shin'ya Yamaguchi, Shoichiro Takeda +4
This paper addresses the tradeoff between standard accuracy on clean examples and robustness against adversarial examples in deep neural networks (DNNs). Although adversarial train…
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…
Learning Robust Convolutional Neural Networks with Relevant Feature Focusing via Explanations
Kazuki Adachi, Shin'ya Yamaguchi
Existing image recognition techniques based on convolutional neural networks (CNNs) basically assume that the training and test datasets are sampled from i.i.d distributions. Howev…
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…
F-Drop&Match: GANs with a Dead Zone in the High-Frequency Domain
Shin'ya Yamaguchi, Sekitoshi Kanai
Generative adversarial networks built from deep convolutional neural networks (GANs) lack the ability to exactly replicate the high-frequency components of natural images. To allev…
Constraining Logits by Bounded Function for Adversarial Robustness
Sekitoshi Kanai, Masanori Yamada, Shin'ya Yamaguchi +2
We propose a method for improving adversarial robustness by addition of a new bounded function just before softmax. Recent studies hypothesize that small logits (inputs of softmax)…
Effective Data Augmentation with Multi-Domain Learning GANs
Shin'ya Yamaguchi, Sekitoshi Kanai, Takeharu Eda
For deep learning applications, the massive data development (e.g., collecting, labeling), which is an essential process in building practical applications, still incurs seriously…
Image Enhanced Rotation Prediction for Self-Supervised Learning
Shin'ya Yamaguchi, Sekitoshi Kanai, Tetsuya Shioda +1
The rotation prediction (Rotation) is a simple pretext-task for self-supervised learning (SSL), where models learn useful representations for target vision tasks by solving pretext…