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The support set is a key to providing conditional prior for fast adaption ofthe model in few-shot tasks. But the strict form of support set makes itsconstruction actually difficult in practical application. Motivated by ANIL, werethink the role of adaption in the feature extractor of CNAPs, which is astate-of-the-art representative few-shot method. To investigate the role,Almost Zero-Shot (AZS) task is designed by fixing the support set to replacethe common scheme, which provides corresponding support sets for the differentconditional prior of different tasks. The AZS experiment results infer that theadaptation works little in the feature extractor. However, CNAPs cannot berobust to randomly selected support sets and perform poorly on some datasets ofMeta-Dataset because of its scattered mean embeddings responded by the simplemean operator. To enhance the robustness of CNAPs, Canonical Mean Filter (CMF)module is proposed to make the mean embeddings intensive and stable in featurespace by mapping the support sets into a canonical form. CMFs make CNAPs robustto any fixed support sets even if they are random matrices. This attributionmakes CNAPs be able to remove the mean encoder and the parameter adaptationnetwork at the test stage, while CNAP-CMF on AZS tasks keeps the performancewith one-shot tasks. It leads to a big parameter reduction. Precisely, 40.48\%parameters are dropped at the test stage. Also, CNAP-CMF outperforms CNAPs inone-shot tasks because it addresses inner-task unstable performance problems.Classification performance, visualized and clustering results verify that CMFsmake CNAPs better and simpler.

Canonical Mean Filter for Almost Zero-Shot Multi-Task classification