A neural network model with a classical annotation method has been used on the EXL-50tokamak to predict impending disruption.However,the results revealed issues of overfitting and overconfidence in predictions caused ...A neural network model with a classical annotation method has been used on the EXL-50tokamak to predict impending disruption.However,the results revealed issues of overfitting and overconfidence in predictions caused by inaccurate labeling.To mitigate these issues,an improved training framework has been proposed.In this approach,soft labels from previous training serve as teachers to supervise the further learning process;this has lead to a significant improvement in predictive model performance.Notably,this enhancement is primarily attributed to the coupling effect of the soft labels and correction mechanism.This improved training framework introduces an instance-specific label smoothing method,which reflects a more nuanced model assessment on the likelihood of a disruption.It presents a possible solution to effectively address the challenges associated with accurate labeling across different machines.展开更多
By leveraging data from a fully labeled source domain,unsupervised domain adaptation(UDA)im-proves classification performance on an unlabeled target domain through explicit discrepancy minimization of data distributio...By leveraging data from a fully labeled source domain,unsupervised domain adaptation(UDA)im-proves classification performance on an unlabeled target domain through explicit discrepancy minimization of data distribution or adversarial learning.As an enhancement,category alignment is involved during adaptation to reinforce target feature discrimination by utilizing model prediction.However,there remain unexplored prob-lems about pseudo-label inaccuracy incurred by wrong category predictions on target domain,and distribution deviation caused by overfitting on source domain.In this paper,we propose a model-agnostic two-stage learning framework,which greatly reduces flawed model predictions using soft pseudo-label strategy and avoids overfitting on source domain with a curriculum learning strategy.Theoretically,it successfully decreases the combined risk in the upper bound of expected error on the target domain.In the first stage,we train a model with distribution alignment-based UDA method to obtain soft semantic label on target domain with rather high confidence.To avoid overfitting on source domain,in the second stage,we propose a curriculum learning strategy to adaptively control the weighting between losses from the two domains so that the focus of the training stage is gradually shifted from source distribution to target distribution with prediction confidence boosted on the target domain.Extensive experiments on two well-known benchmark datasets validate the universal effectiveness of our proposed framework on promoting the performance of the top-ranked UDA algorithms and demonstrate its consistent su-perior performance.展开更多
基金supported by National Natural Science Foundation of China(Nos.12175277 and 11975271)the National Key R&D Program of China(No.2022YFE 03050003)。
文摘A neural network model with a classical annotation method has been used on the EXL-50tokamak to predict impending disruption.However,the results revealed issues of overfitting and overconfidence in predictions caused by inaccurate labeling.To mitigate these issues,an improved training framework has been proposed.In this approach,soft labels from previous training serve as teachers to supervise the further learning process;this has lead to a significant improvement in predictive model performance.Notably,this enhancement is primarily attributed to the coupling effect of the soft labels and correction mechanism.This improved training framework introduces an instance-specific label smoothing method,which reflects a more nuanced model assessment on the likelihood of a disruption.It presents a possible solution to effectively address the challenges associated with accurate labeling across different machines.
基金the 111 Project(No.BP0719010)the Project of the Science and Technology Commission of Shanghai Municipality(No.18DZ2270700)。
文摘By leveraging data from a fully labeled source domain,unsupervised domain adaptation(UDA)im-proves classification performance on an unlabeled target domain through explicit discrepancy minimization of data distribution or adversarial learning.As an enhancement,category alignment is involved during adaptation to reinforce target feature discrimination by utilizing model prediction.However,there remain unexplored prob-lems about pseudo-label inaccuracy incurred by wrong category predictions on target domain,and distribution deviation caused by overfitting on source domain.In this paper,we propose a model-agnostic two-stage learning framework,which greatly reduces flawed model predictions using soft pseudo-label strategy and avoids overfitting on source domain with a curriculum learning strategy.Theoretically,it successfully decreases the combined risk in the upper bound of expected error on the target domain.In the first stage,we train a model with distribution alignment-based UDA method to obtain soft semantic label on target domain with rather high confidence.To avoid overfitting on source domain,in the second stage,we propose a curriculum learning strategy to adaptively control the weighting between losses from the two domains so that the focus of the training stage is gradually shifted from source distribution to target distribution with prediction confidence boosted on the target domain.Extensive experiments on two well-known benchmark datasets validate the universal effectiveness of our proposed framework on promoting the performance of the top-ranked UDA algorithms and demonstrate its consistent su-perior performance.
