Inductive Unsupervised Domain Adaptation for Few-Shot Classification via Clustering
Table 3: An ablation study of our proposed framework on the FewRel 2.0 dataset.
['Model', '5-Way-1-Shot', '5-Way-5-Shot', '10-Way-1-Shot', '10-Way-5-Shot']
From these ablations, we find that: (1) Removing the pseudo-labeled target-domain data hurts the result by 4.73%, 8.36%, 4.80% and 9.97% in four scenarios, respectively, which indicates that training the network with clustering-generated pseudo labels is vital for domain adaptation. (2) By introducing the similarity entropy minimization method (CPM-S), we can mine the latent cluster structure of unlabeled target-domain instances, which is beneficial to high-quality pseudo label generation. (3) When we remove CPM-A, the score drops by 3.81% on average, which demonstrates the effectiveness of adversarial distribution alignment over similar classes across domains.