Combined Learning for Similar Tasks with Domain-Switching Networks
We introduce a domain switch for deep neural networks that enables to re-weight convolutional kernels for an input of a known domain. This technique is designed to address re-occurring tasks across multiple domains that are known at runtime and to incorporate them into a single, domain-spanning network. We evaluate this approach in three distinct tasks, namely combined cell nuclei analysis across different stains and fluorescence images, facial landmark detection in grayscale and thermal infrared images, and the BraTS challenge where we treat different recording institutions as domains. We found that conventional U-nets trained on multiple domains perform similar to domain-specific U-nets. Our method improves the results in facial landmark detection significantly, but no change is measured in the other two experiments compared to multi-domain U-nets.
KeywordsDeep learning Multi-modality Multi-domain
This work was partially supported by the Federal Ministry of Education and Research – BMBF, Germany (grant no. 031 B0006B) and by the German Research Foundation – DFG (grant no. ME3737/3-1).
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