Prompt Description
The plan is to use a BERT-based approach for semi-supervised learning of anomalous text, which is divided into four parts: unsupervised pre-processing, clustering algorithms, pseudo-label incorporation, and active learning using large models.
![APPROVED
This section focuses on experimental verification and model evolution. The core aspects include: 1. Evolutionary Strategy Fusion: Integrating the Analytic Hierarchy Process (AHP) with adversarial distillation heterogeneous techniques to achieve self-evolution of the anti-interference model. 2. Model Library Generation: Applying graph-driven pruning and multi-metric channel compression to generate various intelligent anti-interference decision model variants. 3. Layered Testing: Conducting dual verification of basic performance and adversarial robustness on embedded devices and GPU server platforms.
The final content includes the following: 5. [Bottom Illustration/Formula]: Depicting a **"model evolution funnel"**: Original large model $\xrightarrow{Pruning/Distillation}$ Evolved Models V1/V2/V3 $\xrightarrow{Testing}$ Optimal Strategy.](/_next/image?url=https%3A%2F%2Fpub-8c0ddfa5c0454d40822bc9944fe6f303.r2.dev%2Fai-drawings%2Fd4HJgWhDq5YPdBs9p3hto7zsqac9CQfA%2Ffe88eb94-ac9e-440f-8ba6-70151a779b37%2F53b4a50e-b721-4fe0-8cbb-60ab4bb29cd7.png&w=3840&q=75)
