Sluice network transfer learning jointly detects infected clients and malicious domains from HTTPS traffic, outperforming separate models and identifying previously unknown threats.
Learning Multiple Tasks with Multilinear Relationship Networks
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abstract
Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks. Since deep features eventually transition from general to specific along deep networks, a fundamental problem of multi-task learning is how to exploit the task relatedness underlying parameter tensors and improve feature transferability in the multiple task-specific layers. This paper presents Multilinear Relationship Networks (MRN) that discover the task relationships based on novel tensor normal priors over parameter tensors of multiple task-specific layers in deep convolutional networks. By jointly learning transferable features and multilinear relationships of tasks and features, MRN is able to alleviate the dilemma of negative-transfer in the feature layers and under-transfer in the classifier layer. Experiments show that MRN yields state-of-the-art results on three multi-task learning datasets.
fields
cs.LG 2representative citing papers
Multi-task learning succeeds in deep networks by sharing parameters across related tasks and selecting helpful auxiliary tasks to improve generalization and efficiency.
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Joint Detection of Malicious Domains and Infected Clients
Sluice network transfer learning jointly detects infected clients and malicious domains from HTTPS traffic, outperforming separate models and identifying previously unknown threats.
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Multi-task learning succeeds in deep networks by sharing parameters across related tasks and selecting helpful auxiliary tasks to improve generalization and efficiency.