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ニューラルシーンの表現とレンダリング

S M Ali Eslami¹, Danilo Jimenez Rezende², Frederic Besse²

¹DeepMind, 5 New Street Square, London EC4A 3TW, UK. aeslami@google.com.

Science (New York, N.Y.)

|June 16, 2018

まとめ

機械はGenerative Query Network (GQN) を使って人間のラベルなしでシーンの表現を学習できますこのAIフレームワークは機械が自体のセンサーデータから学習することで自律的に環境を理解できるようにします

科学分野:

人工知能
コンピュータ・ビジョン
機械学習

背景:

シーンの表現はインテリジェントなシステムにとって極めて重要です.
ニューラルネットワークは有効ですが,通常,ラベル付きの大きなデータセットが必要です.
人工知能における重要な課題は人工知能によるラベリングへの依存を減らすことです

研究の目的:

無監督のシーンの表現学習のための新しい枠組みを導入します.
機械が自分のセンサーデータだけでシーンの表現を学習できるようにする.
人工知能が人間のラベルや事前の領域知識なしに環境を理解するための方法を開発する.

主な方法:

生成クエリネットワーク (GQN) を開発した.
GQNは複数の視点から画像を処理し,内部シーンの表現を構築します.
フレームワークは新しい視点からシーンの外観を予測します

主要な成果:

人間のラベルなしで成功した表現学習を証明した.
観察されていない視点から現場の外観を予測する能力を示した.
GQNは自律的にシーンの表現を学習します.

結論:

GQNは,人間の監督や領域の専門知識なしに,表現学習を容易にする.
このアプローチは,自律的に周囲の環境を認識し理解することを学ぶことができる AI システムの開発を進めます.
より有能で適応力のある機械に道を開くのです

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