Contextualizing Meta-Learning via Learning to Decompose
Han-Jia Ye, Da-Wei Zhou, Lanqing Hong, Zhenguo Li, Xiu-Shen Wei, De‐Chuan Zhan · IEEE Transactions on Pattern Analysis and Machine Intelligence · 2023
Meta-learning has emerged as an efficient approach for constructing target models based on support sets. For example, the meta-learned embeddings enable the construction of target nearest-neighbor classifiers for specific tasks by pulling instances closer to their same-class neighbors. However, a single instance can be annotated from various latent attributes, making visually similar instances inside or across support sets have different labels and diverse relationships with others. Consequently, a uniform meta-learned strategy for inferring the target model from the support set fails to capture the instance-wise ambiguous similarity. To this end, we propose Learning to Decompose Network (LeadNet) tocontextualizethe meta-learned “support-to-target” strategy, leveraging the context of instances with one or mixed latent attributes in a support set. In particular, the comparison relationship between instances is decomposed w.r.t. multiple embedding spaces.LeadNetlearns to automatically select the strategy associated with the right attribute via incorporatingthe change of comparison across contextswith polysemous embeddings. We demonstrate the superiority ofLeadNetin various applications, including exploring multiple views of confusing data, out-of-distribution recognition, and few-shot image classification.