30 citations · 193 across the 34 of their papers we have counts for
17 papers · 1 filter
Self-supervised Learning of 3D Objects from Natural Images
Hiroharu Kato, Tatsuya Harada
We present a method to learn single-view reconstruction of the 3D shape, pose, and texture of objects from categorized natural images in a self-supervised manner. Since this is a s…
Domain Generalization Using a Mixture of Multiple Latent Domains
Toshihiko Matsuura, Tatsuya Harada
When domains, which represent underlying data distributions, vary during training and testing processes, deep neural networks suffer a drop in their performance. Domain generalizat…
Noise Robust Generative Adversarial Networks
Takuhiro Kaneko, Tatsuya Harada
Generative adversarial networks (GANs) are neural networks that learn data distributions through adversarial training. In intensive studies, recent GANs have shown promising result…
Unsupervised Keyword Extraction for Full-sentence VQA
Kohei Uehara, Tatsuya Harada
In the majority of the existing Visual Question Answering (VQA) research, the answers consist of short, often single words, as per instructions given to the annotators during datas…
Multi-Stage Pathological Image Classification using Semantic Segmentation
Shusuke Takahama, Yusuke Kurose, Yusuke Mukuta +5
Histopathological image analysis is an essential process for the discovery of diseases such as cancer. However, it is challenging to train CNN on whole slide images (WSIs) of gigap…
A General Upper Bound for Unsupervised Domain Adaptation
Dexuan Zhang, Tatsuya Harada
In this work, we present a novel upper bound of target error to address the problem for unsupervised domain adaptation. Recent studies reveal that a deep neural network can learn t…