ANNOTATION DEVICE AND METHOD
To provide technology for realizing efficient labeling regarding a machine learning model for executing a task constituted of a plurality of subtasks.SOLUTION: An annotation device 1 for realizing labeling of machine learning comprises: a machine learning module 103 with a teacher having a plurality...
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10.04.2024
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Abstract | To provide technology for realizing efficient labeling regarding a machine learning model for executing a task constituted of a plurality of subtasks.SOLUTION: An annotation device 1 for realizing labeling of machine learning comprises: a machine learning module 103 with a teacher having a plurality of subtask modules 104 for performing learning to each of a plurality of subtasks constituting a task of the machine learning by using data with a label to estimate an estimation label to data without a label; a subtask mutual influence degree calculation module 107 which calculates an influence degree showing degrees of influence to other subtask modules 104 by learning by the subtask modules 104; and a label correction module 110 which corrects the estimation label according to a confirmation result to the estimation label presented according to the total priority score based on the influence degree.SELECTED DRAWING: Figure 1
【課題】複数サブタスクから構成されるタスクを実行する機械学習モデルに関して、効率的なラベル付けを実現する技術を提供する。【解決手段】機械学習のラベル付けを実現するアノテーション装置1において、前記機械学習のタスクを構成する複数のサブタスクのそれぞれに対して、ラベル付きデータを用いて学習し、ラベルなしデータに対する推定ラベルを推定する複数のサブタスクモジュール104を有する教師あり機械学習モジュール103と、サブタスクモジュール104での学習による他のサブタスクモジュール104への影響の度合いを示す影響度を計算するサブタスク相互影響度計算モジュール107と、影響度に基づく総合優先スコアに応じて提示された推定ラベルに対する確認結果に応じて、当該推定ラベルを修正するラベル修正モジュール110を有する。【選択図】図1 |
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AbstractList | To provide technology for realizing efficient labeling regarding a machine learning model for executing a task constituted of a plurality of subtasks.SOLUTION: An annotation device 1 for realizing labeling of machine learning comprises: a machine learning module 103 with a teacher having a plurality of subtask modules 104 for performing learning to each of a plurality of subtasks constituting a task of the machine learning by using data with a label to estimate an estimation label to data without a label; a subtask mutual influence degree calculation module 107 which calculates an influence degree showing degrees of influence to other subtask modules 104 by learning by the subtask modules 104; and a label correction module 110 which corrects the estimation label according to a confirmation result to the estimation label presented according to the total priority score based on the influence degree.SELECTED DRAWING: Figure 1
【課題】複数サブタスクから構成されるタスクを実行する機械学習モデルに関して、効率的なラベル付けを実現する技術を提供する。【解決手段】機械学習のラベル付けを実現するアノテーション装置1において、前記機械学習のタスクを構成する複数のサブタスクのそれぞれに対して、ラベル付きデータを用いて学習し、ラベルなしデータに対する推定ラベルを推定する複数のサブタスクモジュール104を有する教師あり機械学習モジュール103と、サブタスクモジュール104での学習による他のサブタスクモジュール104への影響の度合いを示す影響度を計算するサブタスク相互影響度計算モジュール107と、影響度に基づく総合優先スコアに応じて提示された推定ラベルに対する確認結果に応じて、当該推定ラベルを修正するラベル修正モジュール110を有する。【選択図】図1 |
Author | MORIMOTO YASUTSUGU MORIOKA TOMOAKI MUTO KAZUO |
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Snippet | To provide technology for realizing efficient labeling regarding a machine learning model for executing a task constituted of a plurality of subtasks.SOLUTION:... |
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SubjectTerms | CALCULATING COMPUTER SYSTEMS BASED ON SPECIFIC COMPUTATIONAL MODELS COMPUTING COUNTING PHYSICS |
Title | ANNOTATION DEVICE AND METHOD |
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