Learning to Optimize Domain Specific Normalization for Domain Generalization
We propose a simple but effective multi-source domain generalization technique based on deep neural networks by incorporating optimized normalization layers that are specific to individual domains. Our approach employs multiple normalization methods while learning separate affine parameters per doma...
Saved in:
Main Authors | , , , , , |
---|---|
Format | Journal Article |
Language | English |
Published |
09.07.2019
|
Subjects | |
Online Access | Get full text |
Cover
Loading…
Summary: | We propose a simple but effective multi-source domain generalization
technique based on deep neural networks by incorporating optimized
normalization layers that are specific to individual domains. Our approach
employs multiple normalization methods while learning separate affine
parameters per domain. For each domain, the activations are normalized by a
weighted average of multiple normalization statistics. The normalization
statistics are kept track of separately for each normalization type if
necessary. Specifically, we employ batch and instance normalizations in our
implementation to identify the best combination of these two normalization
methods in each domain. The optimized normalization layers are effective to
enhance the generalizability of the learned model. We demonstrate the
state-of-the-art accuracy of our algorithm in the standard domain
generalization benchmarks, as well as viability to further tasks such as
multi-source domain adaptation and domain generalization in the presence of
label noise. |
---|---|
DOI: | 10.48550/arxiv.1907.04275 |