A new hope for network model generalization. (arXiv:2207.05843v1 [cs.NI])

Generalizing machine learning (ML) models for network traffic dynamics tends
to be considered a lost cause. Hence, for every new task, we often resolve to
design new models and train them on model-specific datasets collected, whenever
possible, in an environment mimicking the model's deployment. This approach
essentially gives up on generalization. Yet, an ML architecture
called_Transformer_ has enabled previously unimaginable generalization in other
domains. Nowadays, one can download a model pre-trained on massive datasets and
only fine-tune it for a …

arxiv model generalization network