상호 정보 정규화를 통한 지식 증류 기반 단일 소스 도메인 일반화

학위논문(석사) -- 서울대학교대학원 : 데이터사이언스대학원 데이터사이언스학과, 2023. 8. 이상학.Machine learning frequently suffers from the discrepancy in data distribution, commonly known as domain shift. Single-source Domain Generalization (sDG) is a task designed to simulate domain shift artificially, in order to train a model that can generalize well to multiple unseen target domains from a single source domain. A popular approach is to learn robustness via the alignment of samples generated by data augmentation. However, prior works frequently overlooked what can be learned through such alignment. In this paper, we study the effectiveness of augmentation-based sDG methods by connecting recent identifiability results by Von Kugelgen et al. (2021). We highlight the overlooked issues in using augmentation for OOD generalization and search ways to alleviate them. We introduce a novel sDG method that leverages pretrained models to guide the learning process via a feature-level regularization of mutual information, which we name PROF (Progressive mutual information Regularization for Online distillation of Frozen oracles). PROF can be added to conventional augmentation-based methods to dampen the fluctuation of the OOD performance. We further introduce a data-effective alignment objective as well as a novel augmentation method for fine-grained simulation of domain shift.일반적으로, 머신러닝 모델은 데이터 분포 상의 변화에 취약한 경향을 보인다. 단일 소스 도메인 일반화(sDG)는 이러한 문제를 해결하기 위해 고안된 연구 태스크로, 인공적으로 설정된 분포 변화에도 강건한 모델을 만드는 것을 목표로 한다. 기존의 sDG 연구는 다양한 데이터 증강 기법을 통해 모델의 일반화 성능을 향상시키는 데 집중하였으나, 이와 같은 증강 기반 접근법의 유효성은 깊이 논의되지 않았다. 본 논문은 최근 Von Kugelgen et al. (2021)의 연구결과를 이용하여 기존에 간과된 증강 기반 접근법의 문제들을 인과적 관점에서 조명하고, 그에 대한 해결책을 탐구한다. 본 연구진은 증강 기반 sDG 방식의 불안정성을 해소하기 위한 "PROF: 상호 정보 정규화를 통한 지식 증류 기반 단일 소스 도메인 일반화" 기법을 제시한다. PROF는 선학습된 모델의 지식을 이용한 증류 기반의 정규화 기법을 통해 모델의 훈련 과정을 지도한다. PROF는 증강 기반 sDG 방식에 추가되어, 모델의 일반화 성능이 안정적으로 증가할 수 있도록 한다. 나아가, 본 논문은 기존 방식에 비해 경제적인 정렬 함수와 개선된 데이터 증강 방식을 제안하였다.Abstract Table of Contents 1. Introduction 1 2. Preliminaries 3 3. Limitations of Augmentation for sDG 5 4. Leveraging Pretrained Models for Domain Invariance 9 4.1. Oracle Regularizer 11 4.2. Learnable Domain Shift Simulators 13 4.3. Multi-Domain Alignment with Redundancy Reduction 14 5. Experiment 16 5.1. Experimental Settings 16 5.2. Implementation Detail 18 5.2.1. Model Architecture 18 5.2.2. Model Training 22 5.2.3. Model Pretraining 23 5.2.4. Hyperparameters 24 5.3. Experimental Results and Analysis 25 5.3.1. Experimental Results 25 5.3.2. Analysis 34 6. Conclusion 37 Bibliography 39 Abstract in Korean 54석

miR-351-5p/Miro2 axis contributes to hippocampal neural progenitor cell death via unbalanced mitochondrial fission

Adult hippocampal neurogenesis supports the structural and functional plasticity of the brain, while its decline is associated with neurodegeneration common in Alzheimer's disease (AD). Although the dysregulation of certain microRNAs (miRNAs) in AD have been observed, the effects of miRNAs on hippocampal neurogenesis are largely unknown. In this study, we demonstrated miR-351-5p as a causative factor in hippocampal neural progenitor cell death through modulation of the mitochondrial guanosine triphosphatase (GTPase), Miro2. Downregulation of Miro2 by siMiro2 induced cell death, similar to miR-351-5p, whereas ectopic Miro2 expression using an adenovirus abolished these effects. Excessively fragmented mitochondria and dysfunctional mitochondria were indexed by decreased mitochondrial potential, and increased reactive oxygen species were identified in miR351-5p-induced cell death. Moreover, subsequent induction of mitophagy via Pink1 and Parkin was observed in the presence of miR-351-5p and siMiro2. The suppression of mitochondrial fission by Mdivi-1 completely inhibited cell death by miR-351-5p. miR-351-5p expression increased whereas the level of Miro2 decreased in the hippocampus of AD model mice, emulating expression in AD patients. Collectively, the data indicate the mitochondrial fission and accompanying mitophagy by miR-351-5p/Miro2 axis as critical in hippocampal neural progenitor cell death, and a potential therapeutic target in AD