9 citations · 19 across the 6 of their papers we have counts for
8 papers · 1 filter
TeachAugment: Data Augmentation Optimization Using Teacher Knowledge
Teppei Suzuki
Optimization of image transformation functions for the purpose of data augmentation has been intensively studied. In particular, adversarial data augmentation strategies, which sea…
Rethinking PointNet Embedding for Faster and Compact Model
Teppei Suzuki, Keisuke Ozawa, Yusuke Sekikawa
PointNet, which is the widely used point-wise embedding method and known as a universal approximator for continuous set functions, can process one million points per second. Nevert…
Superpixel Segmentation via Convolutional Neural Networks with Regularized Information Maximization
Teppei Suzuki
We propose an unsupervised superpixel segmentation method by optimizing a randomly-initialized convolutional neural network (CNN) in inference time. Our method generates superpixel…
Tabulated MLP for Fast Point Feature Embedding
Yusuke Sekikawa, Teppei Suzuki
Aiming at a drastic speedup for point-data embeddings at test time, we propose a new framework that uses a pair of multi-layer perceptron (MLP) and look-up table (LUT) to transform…
Adversarial Transformations for Semi-Supervised Learning
Teppei Suzuki, Ikuro Sato
We propose a Regularization framework based on Adversarial Transformations (RAT) for semi-supervised learning. RAT is designed to enhance robustness of the output distribution of c…
Drive Video Analysis for the Detection of Traffic Near-Miss Incidents
Hirokatsu Kataoka, Teppei Suzuki, Shoko Oikawa +2
Because of their recent introduction, self-driving cars and advanced driver assistance system (ADAS) equipped vehicles have had little opportunity to learn, the dangerous traffic (…