The article presents the Deep Mutual Learning (DML) framework tailored for multi-task networks, showcasing its adaptability across different backbone models. Through comprehensive offline experiments and ablation studies, the framework's efficacy is affirmed, revealing how each new module contributes to overall performance. DML not only performs well on benchmark datasets but has also achieved significant gains in online settings, illustrating its practical applicability. The research is backed by a robust set of references, underscoring previous works in deep learning and multitask learning frameworks.
The Deep Mutual Learning framework (DML) significantly enhances the effectiveness of multi-task networks across various base models, verified through extensive offline experiments.
Our ablation studies revealed the significant contributions of newly introduced modules in the DML, ultimately leading to large performance gains in online deployment.
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