MGNet: Mutual-guidance network for few-shot semantic segmentation

In most batch processes, the correlations of process variables present multi‐stage characteristic as the process progress and operating conditions change. The methods building a local model at each stage ignore the potential correlations among stages, resulting in poor quality prediction of batch process. To solve this problem, a batch process quality prediction method based on multi‐stage fusion regression network (MSFRN) is proposed. First, the affine propagation clustering (AP) algorithm is used to automatically divide the stages for batch process without relying on prior knowledge. Second, the input reconstruction error and quality prediction error are organically combined to develop a stacked isomorphic and quality‐driven autoencoder (SIQAE) for each stage, which fully extracts the quality‐related features for each stage while reducing the input cumulative loss. Then, the self‐attention mechanism is used to integrate the quality‐related features of each stage so as to obtain global features which consider the correlations among stages. Finally, the global features are input into the fully connected regression layer to predict the quality variables of batch process. The effectiveness of the proposed method was verified by applying on penicillin fermentation process.

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“…The penicillin fermentation process has been widely used in the modelling, monitoring and control of batch processes. [ 49–51 ]…”

Section: Case Studymentioning

confidence: 99%

Multi‐stage fusion regression network for quality prediction of batch process

Yao

Zhao

et al. 2023

Can J Chem Eng

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“…In the meta-learning process, the meta-task based on the meta-objective and meta-task datasets is to update the base model ( Sun et al, 2019 ; Lang et al, 2022 ). Most meta-learning methods are applied to few-shot tasks ( Chang et al, 2022 ). The model trained by a small number of samples can quickly adapt and master the new few-shot task.…”

Section: Preliminarymentioning

confidence: 99%

Few-shot short utterance speaker verification using meta-learning

Wang

Zhao

Yang

et al. 2023

PeerJ Computer Science

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Short utterance speaker verification (SV) in the actual application is the task of accepting or rejecting the identity claim of a speaker based on a few enrollment utterances. Traditional methods have used deep neural networks to extract speaker representations for verification. Recently, several meta-learning approaches have learned a deep distance metric to distinguish speakers within meta-tasks. Among them, a prototypical network learns a metric space that may be used to compute the distance to the prototype center of speakers, in order to classify speaker identity. We use emphasized channel attention, propagation and aggregation in TDNN (ECAPA-TDNN) to implement the necessary function for the prototypical network, which is a nonlinear mapping from the input space to the metric space for either few-shot SV task. In addition, optimizing only for speakers in given meta-tasks cannot be sufficient to learn distinctive speaker features. Thus, we used an episodic training strategy, in which the classes of the support and query sets correspond to the classes of the entire training set, further improving the model performance. The proposed model outperforms comparison models on the VoxCeleb1 dataset and has a wide range of practical applications.

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CSAANet: An Attention-Based Mechanism for Aligned Few-Shot Semantic Segmentation Network

Wei

Qian

2023

Lecture Notes in Computer Science

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MGNet: Mutual-guidance network for few-shot semantic segmentation

Cited by 12 publications

References 59 publications

Multi‐stage fusion regression network for quality prediction of batch process

Multi‐stage fusion regression network for quality prediction of batch process

Few-shot short utterance speaker verification using meta-learning

CSAANet: An Attention-Based Mechanism for Aligned Few-Shot Semantic Segmentation Network

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