1 citations · 1 across the 3 of their papers we have counts for
3 papers
Fair and Interpretable Deepfake Detection in Videos
Akihito Yoshii, Ryosuke Sonoda, Ramya Srinivasan
Existing deepfake detection methods often exhibit bias, lack transparency, and fail to capture temporal information, leading to biased decisions and unreliable results across diffe…
Practical Insights of Repairing Model Problems on Image Classification
Akihito Yoshii, Susumu Tokumoto, Fuyuki Ishikawa
Additional training of a deep learning model can cause negative effects on the results, turning an initially positive sample into a negative one (degradation). Such degradation is…
NeuRecover: Regression-Controlled Repair of Deep Neural Networks with Training History
Shogo Tokui, Susumu Tokumoto, Akihito Yoshii +4
Systematic techniques to improve quality of deep neural networks (DNNs) are critical given the increasing demand for practical applications including safety-critical ones. The key…