A new hope for network model generalization. (arXiv:2207.05843v2 [cs.NI] UPDATED)

Generalizing machine learning (ML) models for network traffic dynamics tends
to be considered a lost cause. Hence for every new task, we design new models
and train them on model-specific datasets closely mimicking the deployment
environments. 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
specific task and context with comparatively little time and data. These
fine-tuned models are now …

arxiv model generalization network