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

Small RNA-Mediated Quiescence of Transposable Elements in Animals

Transposable elements (TEs) are major components of the intergenic regions of the genome. However, TE transposition has the potential to threaten the reproductive fitness of the organism; therefore, organisms have evolved specialized molecular systems to sense and repress the expression of TEs to stop them from jumping to other genomic loci. Emerging evidence suggests that Argonaute proteins play a critical role in this process, in collaboration with two types of cellular small RNAs: PIWI-interacting RNAs (piRNAs) of the germline and endogenous small interfering RNAs (endo-siRNAs) of the soma, both of which are transcribed from TEs themselves