Virtual Adversarial Training for Semi-Supervised Text Classification. - Takeru Miyato; Andrew M. Dai; Ian J. Goodfellow (CoRR, 2016)

Distributional Smoothing by Virtual Adversarial Examples. - Takeru Miyato; Shin-ichi Maeda; Masanori Koyama; Ken Nakae; Shin Ishii (ICLR (Poster), 2016)

Adversarial Training Methods for Semi-Supervised Text Classification. - Takeru Miyato; Andrew M. Dai; Ian J. Goodfellow (ICLR (Poster), 2017)

Synthetic Gradient Methods with Virtual Forward-Backward Networks. - Takeru Miyato; Daisuke Okanohara; Shin-ichi Maeda; Masanori Koyama (ICLR (Workshop), 2017)

Learning Discrete Representations via Information Maximizing Self-Augmented Training. - Weihua Hu; Takeru Miyato; Seiya Tokui; Eiichi Matsumoto; Masashi Sugiyama (ICML, 2017)

Spectral Norm Regularization for Improving the Generalizability of Deep Learning. - Yuichi Yoshida; Takeru Miyato (CoRR, 2017)

Parameter Reference Loss for Unsupervised Domain Adaptation. - Jiren Jin; Richard G. Calland; Takeru Miyato; Brian K. Vogel; Hideki Nakayama (CoRR, 2017)

Neural Multi-scale Image Compression. - Ken M. Nakanishi; Shin-ichi Maeda; Takeru Miyato; Daisuke Okanohara (ACCV (6), 2018)

cGANs with Projection Discriminator. - Takeru Miyato; Masanori Koyama (ICLR (Poster), 2018)

Spectral Normalization for Generative Adversarial Networks. - Takeru Miyato; Toshiki Kataoka; Masanori Koyama; Yuichi Yoshida (ICLR, 2018)

Collaging on Internal Representations: An Intuitive Approach for Semantic Transfiguration. - Ryohei Suzuki; Masanori Koyama; Takeru Miyato; Taizan Yonetsuji (CoRR, 2018)

Robustness to Adversarial Perturbations in Learning from Incomplete Data. - Amir Najafi 002; Shin-ichi Maeda; Masanori Koyama; Takeru Miyato (NeurIPS, 2019)

Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning. - Takeru Miyato; Shin-ichi Maeda; Masanori Koyama; Shin Ishii (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Unsupervised Discrete Representation Learning. - Weihua Hu; Takeru Miyato; Seiya Tokui; Eiichi Matsumoto; Masashi Sugiyama (Explainable AI, 2019)

Contrastive Representation Learning with Trainable Augmentation Channel. - Masanori Koyama; Kentaro Minami; Takeru Miyato; Yarin Gal (CoRR, 2021)

Unsupervised Learning of Equivariant Structure from Sequences. - Takeru Miyato; Masanori Koyama; Kenji Fukumizu (NeurIPS, 2022)

Invariance-adapted decomposition and Lasso-type contrastive learning. - Masanori Koyama; Takeru Miyato; Kenji Fukumizu (CoRR, 2022)

Neural Fourier Transform: A General Approach to Equivariant Representation Learning. - Masanori Koyama; Kenji Fukumizu; Kohei Hayashi; Takeru Miyato (ICLR, 2024)

GTA: A Geometry-Aware Attention Mechanism for Multi-View Transformers. - Takeru Miyato; Bernhard Jaeger; Max Welling; Andreas Geiger 001 (ICLR, 2024)

Artificial Kuramoto Oscillatory Neurons. - Takeru Miyato; Sindy Löwe; Andreas Geiger 001; Max Welling (ICLR, 2025)