29 citations · 31 across the 7 of their papers we have counts for
11 papers
Parameter Efficient Transfer Learning for Various Speech Processing Tasks
Shinta Otake, Rei Kawakami, Nakamasa Inoue
Fine-tuning of self-supervised models is a powerful transfer learning method in a variety of fields, including speech processing, since it can utilize generic feature representatio…
Can Vision Transformers Learn without Natural Images?
Kodai Nakashima, Hirokatsu Kataoka, Asato Matsumoto +2
Can we complete pre-training of Vision Transformers (ViT) without natural images and human-annotated labels? Although a pre-trained ViT seems to heavily rely on a large-scale datas…
Pre-training without Natural Images
Hirokatsu Kataoka, Kazushige Okayasu, Asato Matsumoto +5
Is it possible to use convolutional neural networks pre-trained without any natural images to assist natural image understanding? The paper proposes a novel concept, Formula-driven…
Initialization Using Perlin Noise for Training Networks with a Limited Amount of Data
Nakamasa Inoue, Eisuke Yamagata, Hirokatsu Kataoka
We propose a novel network initialization method using Perlin noise for training image classification networks with a limited amount of data. Our main idea is to initialize the net…
Semi-Supervised Contrastive Learning with Generalized Contrastive Loss and Its Application to Speaker Recognition
Nakamasa Inoue, Keita Goto
This paper introduces a semi-supervised contrastive learning framework and its application to text-independent speaker verification. The proposed framework employs generalized cont…
Augmented Cyclic Consistency Regularization for Unpaired Image-to-Image Translation
Takehiko Ohkawa, Naoto Inoue, Hirokatsu Kataoka +1
Unpaired image-to-image (I2I) translation has received considerable attention in pattern recognition and computer vision because of recent advancements in generative adversarial ne…