218 citations · 253 across the 5 of their papers we have counts for
7 papers
Robustness to Adversarial Perturbations in Learning from Incomplete Data
Amir Najafi, Shin-ichi Maeda, Masanori Koyama +1
What is the role of unlabeled data in an inference problem, when the presumed underlying distribution is adversarially perturbed? To provide a concrete answer to this question, thi…
Spatially Controllable Image Synthesis with Internal Representation Collaging
Ryohei Suzuki, Masanori Koyama, Takeru Miyato +2
We present a novel CNN-based image editing strategy that allows the user to change the semantic information of an image over an arbitrary region by manipulating the feature-space r…
Neural Multi-scale Image Compression
Ken Nakanishi, Shin-ichi Maeda, Takeru Miyato +1
This study presents a new lossy image compression method that utilizes the multi-scale features of natural images. Our model consists of two networks: multi-scale lossy autoencoder…
Spectral Normalization for Generative Adversarial Networks
Takeru Miyato, Toshiki Kataoka, Masanori Koyama +1
One of the challenges in the study of generative adversarial networks is the instability of its training. In this paper, we propose a novel weight normalization technique called sp…
cGANs with Projection Discriminator
Takeru Miyato, Masanori Koyama
We propose a novel, projection based way to incorporate the conditional information into the discriminator of GANs that respects the role of the conditional information in the unde…
Parameter Reference Loss for Unsupervised Domain Adaptation
Jiren Jin, Richard G. Calland, Takeru Miyato +2
The success of deep learning in computer vision is mainly attributed to an abundance of data. However, collecting large-scale data is not always possible, especially for the superv…