It's challenging to balance the networks stability and plasticity in
continual learning scenarios, considering stability suffers from the update of
model and plasticity benefits from it. Existing works usually focus more on the
stability and restrict the learning plasticity of later tasks to avoid
catastrophic forgetting of learned knowledge. Differently, we propose a
continual learning method named Split2MetaFusion which can achieve better
trade-off by employing a two-stage strategy: splitting and meta-weighted
fusion. In this strategy, a slow model with better stability, and a fast model
with better plasticity are learned sequentially at the splitting stage. Then
stability and plasticity are both kept by fusing the two models in an adaptive
manner. Towards this end, we design an optimizer named Task-Preferred Null
Space Projector(TPNSP) to the slow learning process for narrowing the fusion
gap. To achieve better model fusion, we further design a Dreaming-Meta-Weighted
fusion policy for better maintaining the old and new knowledge simultaneously,
which doesn't require to use the previous datasets. Experimental results and
analysis reported in this work demonstrate the superiority of the proposed
method for maintaining networks stability and keeping its plasticity. Our code
will be released.

我们提出了一种名为 Split2MetaFusion 的连续学习方法，通过采用两阶段策略（分割和元权重融合），同时实现了网络稳定性和可塑性的更好权衡。在实验结果和分析中，证明了该方法在保持网络稳定性和可塑性方面的优越性。

通过网络分割和合并与梦幻元加权模型融合的持续学习

Continual Learning through Networks Splitting and Merging with  Dreaming-Meta-Weighted Model Fusion

Image-based systems have gained popularity owing to their capacity to provide
rich manufacturing status information, low implementation costs and high
acquisition rates. However, the complexity of the image background and various
anomaly patterns pose new challenges to existing matrix decomposition methods,
which are inadequate for modeling requirements. Moreover, the uncertainty of
the anomaly can cause anomaly contamination problems, making the designed model
and method highly susceptible to external disturbances. To address these
challenges, we propose a two-stage strategy anomaly detection method that
detects anomalies by identifying suspected patches (Ano-SuPs). Specifically, we
propose to detect the patches with anomalies by reconstructing the input image
twice: the first step is to obtain a set of normal patches by removing those
suspected patches, and the second step is to use those normal patches to refine
the identification of the patches with anomalies. To demonstrate its
effectiveness, we evaluate the proposed method systematically through
simulation experiments and case studies. We further identified the key
parameters and designed steps that impact the model's performance and
efficiency.

提出了一种两阶段策略异常检测方法 —— 通过识别可疑的补丁（Ano-SuPs）来检测异常。该方法通过重建输入图像两次来检测带有异常补丁的区域，首先剔除可疑补丁以获得一组正常补丁，然后利用这些正常补丁来进一步确定带有异常的补丁。通过仿真实验和案例研究对该方法进行了系统评估，并进一步确定了影响模型性能和效率的关键参数和设计步骤。

Ano-SuPs：通过识别疑似区块进行多尺寸的制造产品异常检测

Ano-SuPs: Multi-size anomaly detection for manufactured products by  identifying suspected patches

Accessing medical literature is difficult for laypeople as the content is
written for specialists and contains medical jargon. Automated text
simplification methods offer a potential means to address this issue. In this
work, we propose a summarize-then-simplify two-stage strategy, which we call
NapSS, identifying the relevant content to simplify while ensuring that the
original narrative flow is preserved. In this approach, we first generate
reference summaries via sentence matching between the original and the
simplified abstracts. These summaries are then used to train an extractive
summarizer, learning the most relevant content to be simplified. Then, to
ensure the narrative consistency of the simplified text, we synthesize
auxiliary narrative prompts combining key phrases derived from the syntactical
analyses of the original text. Our model achieves results significantly better
than the seq2seq baseline on an English medical corpus, yielding 3%~4% absolute
improvements in terms of lexical similarity, and providing a further 1.1%
improvement of SARI score when combined with the baseline. We also highlight
shortcomings of existing evaluation methods, and introduce new metrics that
take into account both lexical and high-level semantic similarity. A human
evaluation conducted on a random sample of the test set further establishes the
effectiveness of the proposed approach. Codes and models are released here:
this https URL.

该研究提出了一种名为 NapSS 的自动文本简化方法，采用基于句子匹配的摘要生成和提取式摘要相结合的策略来帮助医学领域的非专业人士更好地理解专业文献，相对于基准策略，在保持原始叙述连贯性的同时提高了文本简化的词汇和语义相似性，实验结果表明该方法可以更有效地提高非专业人士的阅读效率。