218 citations · 253 across the 7 of their papers we have counts for
5 papers · 1 filter
Neural Fourier Transform: A General Approach to Equivariant Representation Learning
Masanori Koyama, Kenji Fukumizu, Kohei Hayashi +1
Symmetry learning has proven to be an effective approach for extracting the hidden structure of data, with the concept of equivariance relation playing the central role. However, m…
Invariance-adapted decomposition and Lasso-type contrastive learning
Masanori Koyama, Takeru Miyato, Kenji Fukumizu
Recent years have witnessed the effectiveness of contrastive learning in obtaining the representation of dataset that is useful in interpretation and downstream tasks. However, the…
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…
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 Norm Regularization for Improving the Generalizability of Deep Learning
Yuichi Yoshida, Takeru Miyato
We investigate the generalizability of deep learning based on the sensitivity to input perturbation. We hypothesize that the high sensitivity to the perturbation of data degrades t…