Learning Algorithm for Advanced Generative Models
Project/Area Number |
17H04693
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Research Category |
Grant-in-Aid for Young Scientists (A)
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Allocation Type | Single-year Grants |
Research Field |
Intelligent informatics
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Research Institution | The University of Tokyo |
Principal Investigator |
Sato Issei 東京大学, 大学院情報理工学系研究科, 准教授 (90610155)
|
Project Period (FY) |
2017-04-01 – 2021-03-31
|
Project Status |
Completed (Fiscal Year 2020)
|
Budget Amount *help |
¥25,090,000 (Direct Cost: ¥19,300,000、Indirect Cost: ¥5,790,000)
Fiscal Year 2019: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2018: ¥4,810,000 (Direct Cost: ¥3,700,000、Indirect Cost: ¥1,110,000)
Fiscal Year 2017: ¥10,660,000 (Direct Cost: ¥8,200,000、Indirect Cost: ¥2,460,000)
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Keywords | 機械学習 / 生成モデル / 学習アルゴリズム / 表形式 / データ拡張 / ドメイン適応 / 敵対的学習 / 深層学習 / ベイズニューラルネット / 逐次学習 / 離散構造 / 深層信念ネットワーク / 再パラメータ化 / 局所期待勾配 |
Outline of Final Research Achievements |
In recent years, the development of generative models in the field of machine learning has been remarkable, reaching a level where images and videos of people who do not exist in reality are indistinguishable to the human eye. However, learning such generative models differs from learning ordinary discriminative models in that it often requires many heuristics in the learning method due to the instability of learning caused by the difficulty of optimizing the objective function and the non-triviality of formulation caused by the difficulty of evaluating the products. In this research, we have worked on elucidating and mitigating the learning instability, developing a generative model for tabular data, which has not been dealt with in existing research, and applying it to minority data analysis.
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Academic Significance and Societal Importance of the Research Achievements |
生成モデルは通常の機械学習における識別モデルによる予測とはことなり,現実には存在しないデータを生成する.現在では,画像や動画などの生成において人が認識できないレベルまで到達しており,人が数時間数日かけて作成するようなものでも数秒で生成してしまう.これは人の創作活動において大きな変革をもたらす可能性を秘めている.しかし,その学習アルゴリズムは未だに解明できていないことも多く,特に学習の不安定さは問題で,学習アルゴリズムに対して多くのヒューリスティックスと試行錯誤が必要となる.本研究では,学習の安定性に関する基礎研究を行い,また生成モデルの適用範囲を表形式データに拡張し,少数データ解析へ応用した.
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Report
(4 results)
Research Products
(6 results)