Search Results for author: Daiki Ikami

Found 11 papers, 3 papers with code

Rethinking Rotation in Self-Supervised Contrastive Learning: Adaptive Positive or Negative Data Augmentation

1 code implementation23 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.

Contrastive Learning Data Augmentation

Generalized Domain Adaptation

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.

Unsupervised Domain Adaptation

A Novel Perspective for Positive-Unlabeled Learning via Noisy Labels

no code implementations8 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.

Fast and Robust Estimation for Unit-Norm Constrained Linear Fitting Problems

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.

Local and Global Optimization Techniques in Graph-Based Clustering

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.

Clustering

Parallel Grid Pooling for Data Augmentation

1 code implementation30 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.

General Classification Image Augmentation +1

Significance of Softmax-based Features in Comparison to Distance Metric Learning-based Features

no code implementations29 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.

Metric Learning

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