JP2019511777A - 畳み込みニューラルネットワークにおける構造学習 - Google Patents
畳み込みニューラルネットワークにおける構造学習 Download PDFInfo
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Abstract
Description
(拡張現実およびコンピューティングシステムアーキテクチャ)
Claims (14)
- プロセッサを用いて実装される方法であって、
ニューラルネットワークを作成するステップと、
前記ニューラルネットワークから出力を生成するステップと、
前記ニューラルネットワークから低性能層を識別するステップと、
新しいスペシャリスト層を前記低性能層に挿入するステップと、
前記ニューラルネットワークの最上位層に到達するまで繰り返すステップと
を含む、方法。 - 更新されたモデルは、複数の新しいスペシャリスト層と、少なくとも1つのジェネラリスト層とを備える、請求項1に記載の方法。
- 前記新しいスペシャリスト層は、別のスペシャリスト層によってハンドリングされるサブドメインと明確に異なるデータの特定のサブドメインに焦点を当てる、請求項1に記載の方法。
- 複数の損失層が、前記ニューラルネットワークに追加される、請求項1に記載の方法。
- 前記複数の損失層は、前記ニューラルネットワークの各層に追加される、請求項4に記載の方法。
- 予測が、各損失層において生成され、1つまたはそれを上回る混同行列に変換され、前記ニューラルネットワークに関する前記1つまたはそれを上回る混同行列の全てを有するテンソルTを形成する、請求項4に記載の方法。
- Tの構造は、深度および幅の両方の観点から、分析され、前記ニューラルネットワークの既存の構造を修正および強化する、請求項6に記載の方法。
- 前記ニューラルネットワークは、垂直分割および水平分割の両方を受ける、請求項1に記載の方法。
- K分割法が、前記水平分割を実装するために行われる、請求項8に記載の方法。
- 前記ネットワークの各層は、独立して対処され、所与の層は、貪欲選択を行い、層を分割させることによって、分割を受け、これは、訓練損失に関する最良改良を提供する、請求項1に記載の方法。
- オール・オア・ナッシングハイウェイネットワークが、除去されるべき前記ニューラルネットワーク内の層を識別するために採用される、請求項1に記載の方法。
- 前記ニューラルネットワークは、仮想現実または拡張現実システムのために捕捉された画像を分類するために採用される、請求項1に記載の方法。
- システムであって、
プロセッサと、
プログラマブルコードを保持するためのメモリと
を備え、前記プログラマブルコードは、方法1−12のいずれかを実行するための命令を含む、システム。 - コンピュータ可読媒体上で具現化されるコンピュータプログラム製品であって、前記コンピュータ可読媒体は、プロセッサによって実行されると、前記プロセッサに方法1−12のいずれかを実行させる、その上に記憶される命令のシーケンスを有する、コンピュータプログラム製品。
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PCT/US2017/022206 WO2017156547A1 (en) | 2016-03-11 | 2017-03-13 | Structure learning in convolutional neural networks |
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JP2019511777A true JP2019511777A (ja) | 2019-04-25 |
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JP6889728B2 JP6889728B2 (ja) | 2021-06-18 |
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JP2020042897A Active JP6983937B2 (ja) | 2016-03-11 | 2020-03-12 | 畳み込みニューラルネットワークにおける構造学習 |
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US (3) | US10255529B2 (ja) |
EP (1) | EP3427192A4 (ja) |
JP (2) | JP6889728B2 (ja) |
KR (2) | KR20200035499A (ja) |
CN (2) | CN115345278A (ja) |
AU (2) | AU2017230184B2 (ja) |
CA (1) | CA3015658A1 (ja) |
IL (1) | IL261245A (ja) |
WO (1) | WO2017156547A1 (ja) |
Cited By (1)
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