7 citations · 10 across the 3 of their papers we have counts for
7 papers
Thinking the Fusion Strategy of Multi-reference Face Reenactment
Takuya Yashima, Takuya Narihira, Tamaki Kojima
In recent advances of deep generative models, face reenactment -manipulating and controlling human face, including their head movement-has drawn much attention for its wide range o…
Data Cleansing for Deep Neural Networks with Storage-efficient Approximation of Influence Functions
Kenji Suzuki, Yoshiyuki Kobayashi, Takuya Narihira
Identifying the influence of training data for data cleansing can improve the accuracy of deep learning. An approach with stochastic gradient descent (SGD) called SGD-influence to…
Perspectives and Prospects on Transformer Architecture for Cross-Modal Tasks with Language and Vision
Andrew Shin, Masato Ishii, Takuya Narihira
Transformer architectures have brought about fundamental changes to computational linguistic field, which had been dominated by recurrent neural networks for many years. Its succes…
Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
Takuya Narihira, Javier Alonsogarcia, Fabien Cardinaux +14
While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design…
Reference-Based Video Colorization with Spatiotemporal Correspondence
Naofumi Akimoto, Akio Hayakawa, Andrew Shin +1
We propose a novel reference-based video colorization framework with spatiotemporal correspondence. Reference-based methods colorize grayscale frames referencing a user input color…
Out-of-core Training for Extremely Large-Scale Neural Networks With Adaptive Window-Based Scheduling
Akio Hayakawa, Takuya Narihira
While large neural networks demonstrate higher performance in various tasks, training large networks is difficult due to limitations on GPU memory size. We propose a novel out-of-c…