1 code implementation • 23 Oct 2022 • Atsuyuki Miyai, Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa
The semantics of an image can be rotation-invariant or rotation-variant, so whether the rotated image is treated as positive or negative should be determined based on the content of the image.
no code implementations • CVPR 2021 • Yu Mitsuzumi, Go Irie, Daiki Ikami, Takashi Shibata
The key to our approach is self-supervised class-destructive learning, which enables the learning of class-invariant representations and domain-adversarial classifiers without using any domain labels.
no code implementations • 8 Mar 2021 • Daiki Tanaka, Daiki Ikami, Kiyoharu Aizawa
Positive-unlabeled learning refers to the process of training a binary classifier using only positive and unlabeled data.
no code implementations • 3 Nov 2020 • Takumi Kawashima, Qing Yu, Akari Asai, Daiki Ikami, Kiyoharu Aizawa
We propose a new optimization framework for aleatoric uncertainty estimation in regression problems.
no code implementations • ECCV 2020 • Qing Yu, Daiki Ikami, Go Irie, Kiyoharu Aizawa
Semi-supervised learning (SSL) has been proposed to leverage unlabeled data for training powerful models when only limited labeled data is available.
no code implementations • CVPR 2018 • Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
M-estimator using iteratively reweighted least squares (IRLS) is one of the best-known methods for robust estimation.
no code implementations • CVPR 2018 • Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
We propose a local optimization method, which is widely applicable to graph-based clustering cost functions.
1 code implementation • CVPR 2018 • Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
Deep neural networks (DNNs) trained on large-scale datasets have exhibited significant performance in image classification.
Ranked #39 on Image Classification on Clothing1M
1 code implementation • 30 Mar 2018 • Akito Takeki, Daiki Ikami, Go Irie, Kiyoharu Aizawa
Convolutional neural network (CNN) architectures utilize downsampling layers, which restrict the subsequent layers to learn spatially invariant features while reducing computational costs.
no code implementations • 29 Dec 2017 • Shota Horiguchi, Daiki Ikami, Kiyoharu Aizawa
However, in these DML studies, there were no equitable comparisons between features extracted from a DML-based network and those from a softmax-based network.
no code implementations • CVPR 2017 • Daiki Ikami, Toshihiko Yamasaki, Kiyoharu Aizawa
We propose the residual expansion (RE) algorithm: a global (or near-global) optimization method for nonconvex least squares problems.