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Wide Two-Layer Networks can Learn from Adversarial Perturbations
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
Adversarial examples have raised several open questions, such as why they can deceive classifiers and transfer between different models. A prevailing hypothesis to explain these ph…
Language-guided Detection and Mitigation of Unknown Dataset Bias
Zaiying Zhao, Soichiro Kumano, Toshihiko Yamasaki
Dataset bias is a significant problem in training fair classifiers. When attributes unrelated to classification exhibit strong biases towards certain classes, classifiers trained o…
Theoretical Understanding of Learning from Adversarial Perturbations
Soichiro Kumano, Hiroshi Kera, Toshihiko Yamasaki
It is not fully understood why adversarial examples can deceive neural networks and transfer between different networks. To elucidate this, several studies have hypothesized that a…
Data-Driven Prediction of Seismic Intensity Distributions Featuring Hybrid Classification-Regression Models
Koyu Mizutani, Haruki Mitarai, Kakeru Miyazaki +2
Earthquakes are among the most immediate and deadly natural disasters that humans face. Accurately forecasting the extent of earthquake damage and assessing potential risks can be…