Cross-domain Few-shot Image Classification Based on Domain-channel Knowledge Discriminative Network
Existing cross-domain few-shot classification models are limited by the interference of domain-specific factors,resulting in poor cross-domain performance.To overcome this problem,a channel knowledge discriminative network for cross-domain few-shot image classification is proposed.Specifically,the proposed learning framework contains a stochastic Gaussian affine module and a channel knowledge discrimination module.In the stochastic gaussian affine module,we salien-tise the domain-invariant information in the feature map by Gaussian perturbing sufficient statistics of the features to generate a new feature distribution that is distinct from the source domain data distribution.In the channel knowledge discrimination module,the feature maps before and after enhancement are fed into the domain discriminator to guide the model to distin-guish and extract the domain-invariant features therein,thus improving the model generalisation capability.Finally,we con-duct experiments on two target datasets and the results validate the effectiveness of the proposed method.
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