Maximum Classifier Discrepancy for Unsupervised Domain Adaptation

In this work, we present a method for unsupervised domain adaptation. Many adversarial learning methods train domain classifier networks to distinguish the features as either a source or target and train a feature generator network to mimic the discriminator. Two problems exist with these methods. First, the domain classifier only tries to distinguish the features as a source or target and thus does not consider task-specific decision boundaries between classes. Therefore, a trained generator can generate ambiguous features near class boundaries. Second, these methods aim to completely match the feature distributions between different domains, which is difficult because of each domain's characteristics. To solve these problems, we introduce a new approach that attempts to align distributions of source and target by utilizing the task-specific decision boundaries. We propose to maximize the discrepancy between two classifiers' outputs to detect target samples that are far from the support of the source. A feature generator learns to generate target features near the support to minimize the discrepancy. Our method outperforms other methods on several datasets of image classification and semantic segmentation. The codes are available at \url{https://github.com/mil-tokyo/MCD_DA}Comment: Accepted to CVPR2018 Oral, Code is available at https://github.com/mil-tokyo/MCD_D

Automatic ballast control referring to the inner pressure at the nadir of the balloon

For long duration balloon flights, when the balloon is outside the tele-command range, it is necessary to control the altitude automatically since the temperature of the lifting gas decreases at sunset. Here, we propose a new method referring to the inner pressure at the nadir of a balloon. The inner pressure is approximately proportional to the amount of descent from the level altitude and to the atmospheric pressure at the level altitude. This method has advantages in its simplic- ity and does not require prior information of the level altitude

Open Set Domain Adaptation by Backpropagation

Numerous algorithms have been proposed for transferring knowledge from a label-rich domain (source) to a label-scarce domain (target). Almost all of them are proposed for a closed-set scenario, where the source and the target domain completely share the class of their samples. We call the shared class the \doublequote{known class.} However, in practice, when samples in target domain are not labeled, we cannot know whether the domains share the class. A target domain can contain samples of classes that are not shared by the source domain. We call such classes the \doublequote{unknown class} and algorithms that work well in the open set situation are very practical. However, most existing distribution matching methods for domain adaptation do not work well in this setting because unknown target samples should not be aligned with the source. In this paper, we propose a method for an open set domain adaptation scenario which utilizes adversarial training. A classifier is trained to make a boundary between the source and the target samples whereas a generator is trained to make target samples far from the boundary. Thus, we assign two options to the feature generator: aligning them with source known samples or rejecting them as unknown target samples. This approach allows extracting features that separate unknown target samples from known target samples. Our method was extensively evaluated in domain adaptation setting and outperformed other methods with a large margin in most settings.Comment: Accepted by ECCV201

Observations of High Energy Cosmic-Ray Electrons from 30 GeV to 3 TeV with Emulsion Chambers

We have performed a series of cosmic-ray electron observations using the balloon-borne emulsion chambers since 1968. While we previously reported the results from subsets of the exposures, the final results of the total exposures up to 2001 are presented here. Our successive experiments have yielded the total exposure of 8.19 m^2 sr day at the altitudes of 4.0 - 9.4 g/cm^2. The performance of the emulsion chambers was examined by accelerator beam tests and Monte-Carlo simulations, and the on-board calibrations were carried out by using the flight data. In this work we present the cosmic-ray electron spectrum in the energy range from 30 GeV to 3 TeV at the top of the atmosphere, which is well represented by a power-law function with an index of -3.28+-0.10. The observed data can be also interpreted in terms of diffusive propagation models. The evidence of cosmic-ray electrons up to 3 TeV suggests the existence of cosmic-ray electron sources at distances within ~1 kpc and times within ~1x10^5 yr ago.Comment: 38 pages, 10 figures, 3 tables, Accepted for publication in Ap