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Learning Embedding Adaptation for Few-Shot Learning | | Han-Jia Ye
; Hexiang Hu
; De-Chuan Zhan
; Fei Sha
; | | Date: |
10 Dec 2018 | | Abstract: | Learning with limited data is a key challenge for visual recognition.
Few-shot learning methods address this challenge by learning an instance
embedding function from seen classes and apply the function to instances from
unseen classes with limited labels. This style of transfer learning is
task-agnostic: the embedding function is not learned optimally discriminative
with respect to the unseen classes, where discerning among them is the target
task. In this paper, we propose a novel approach to adapt the embedding model
to the target classification task, yielding embeddings that are task-specific
and are discriminative. To this end, we employ a type of self-attention
mechanism called Transformer to transform the embeddings from task-agnostic to
task-specific by focusing on relating instances from the test instances to the
training instances in both seen and unseen classes. Our approach also extends
to both transductive and generalized few-shot classification, two important
settings that have essential use cases. We verify the effectiveness of our
model on two standard benchmark few-shot classification datasets ---
MiniImageNet and CUB, where our approach demonstrates state-of-the-art
empirical performance. | | Source: | arXiv, 1812.3664 | | Services: | Forum | Review | PDF | Favorites | |
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