49 citations · 119 across the 9 of their papers we have counts for
8 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…
Contrastive Representation Learning with Trainable Augmentation Channel
Masanori Koyama, Kentaro Minami, Takeru Miyato +1
In contrastive representation learning, data representation is trained so that it can classify the image instances even when the images are altered by augmentations. However, depen…
When is invariance useful in an Out-of-Distribution Generalization problem ?
Masanori Koyama, Shoichiro Yamaguchi
The goal of Out-of-Distribution (OOD) generalization problem is to train a predictor that generalizes on all environments. Popular approaches in this field use the hypothesis that…
Meta Learning as Bayes Risk Minimization
Shin-ichi Maeda, Toshiki Nakanishi, Masanori Koyama
Meta-Learning is a family of methods that use a set of interrelated tasks to learn a model that can quickly learn a new query task from a possibly small contextual dataset. In this…
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…