173 citations · 260 across the 30 of their papers we have counts for
31 papers
Statistically Significant Concept-based Explanation of Image Classifiers via Model Knockoffs
Kaiwen Xu, Kazuto Fukuchi, Youhei Akimoto +1
A concept-based classifier can explain the decision process of a deep learning model by human-understandable concepts in image classification problems. However, sometimes concept-b…
Heterogeneous Domain Adaptation with Positive and Unlabeled Data
Junki Mori, Ryo Furukawa, Isamu Teranishi +1
Heterogeneous unsupervised domain adaptation (HUDA) is the most challenging domain adaptation setting where the feature spaces of source and target domains are heterogeneous, and t…
Instance-Level Trojan Attacks on Visual Question Answering via Adversarial Learning in Neuron Activation Space
Yuwei Sun, Hideya Ochiai, Jun Sakuma
Trojan attacks embed perturbations in input data leading to malicious behavior in neural network models. A combination of various Trojans in different modalities enables an adversa…
Few-Shot Image-to-Semantics Translation for Policy Transfer in Reinforcement Learning
Rei Sato, Kazuto Fukuchi, Jun Sakuma +1
We investigate policy transfer using image-to-semantics translation to mitigate learning difficulties in vision-based robotics control agents. This problem assumes two environments…
CAMRI Loss: Improving Recall of a Specific Class without Sacrificing Accuracy
Daiki Nishiyama, Kazuto Fukuchi, Youhei Akimoto +1
In real-world applications of multi-class classification models, misclassification in an important class (e.g., stop sign) can be significantly more harmful than in other classes (…
Demographic Parity Constrained Minimax Optimal Regression under Linear Model
Kazuto Fukuchi, Jun Sakuma
We explore the minimax optimal error associated with a demographic parity-constrained regression problem within the context of a linear model. Our proposed model encompasses a broa…