1 citations · 3 across the 11 of their papers we have counts for
12 papers
Partial AUC Maximization from Positive-unlabeled Data
Atsutoshi Kumagai, Tomoharu Iwata, Taishi Nishiyama +3
The partial area under the receiver operating characteristic curve (pAUC) is an important performance metric for binary classification that summarizes true positive rates within a…
Importance Weighting for Unlabeled-unlabeled Learning under Distribution Shift
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi +3
Unlabeled-unlabeled (UU) learning allows us to learn a binary classifier from two sets of unlabeled data with different class-priors. It is a general framework because it includes…
AUC Maximization from Biased Positive-unlabeled Data with Confidence
Atsutoshi Kumagai, Tomoharu Iwata, Hiroshi Takahashi +3
Maximizing the area under the receiver operating characteristic curve (AUC) is a standard approach to imbalanced binary classification. Although positive and negative data are requ…
Uniformity First: Uniformity-aware Test-time Adaptation of Vision-language Models against Image Corruption
Kazuki Adachi, Shin'ya Yamaguchi, Tomoki Hamagami
Pre-trained vision-language models, such as contrastive language-image pre-training (CLIP), have demonstrated a remarkable generalizability, enabling a wide range of applications,…
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…
Test-time Adaptation for Regression by Subspace Alignment
Kazuki Adachi, Shin'ya Yamaguchi, Atsutoshi Kumagai +1
This paper investigates test-time adaptation (TTA) for regression, where a regression model pre-trained in a source domain is adapted to an unknown target distribution with unlabel…
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…
Test-time Adaptation Meets Image Enhancement: Improving Accuracy via Uncertainty-aware Logit Switching
Shohei Enomoto, Naoya Hasegawa, Kazuki Adachi +4
Deep neural networks have achieved remarkable success in a variety of computer vision applications. However, there is a problem of degrading accuracy when the data distribution shi…
Test-time Similarity Modification for Person Re-identification toward Temporal Distribution Shift
Kazuki Adachi, Shohei Enomoto, Taku Sasaki +1
Person re-identification (re-id), which aims to retrieve images of the same person in a given image from a database, is one of the most practical image recognition applications. In…
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…
Fast Regularized Discrete Optimal Transport with Group-Sparse Regularizers
Yasutoshi Ida, Sekitoshi Kanai, Kazuki Adachi +2
Regularized discrete optimal transport (OT) is a powerful tool to measure the distance between two discrete distributions that have been constructed from data samples on two differ…
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…