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Associative Alignment for Few-shot Image Classification
[article]
2020
arXiv
pre-print
Few-shot image classification aims at training a model from only a few examples for each of the "novel" classes. This paper proposes the idea of associative alignment for leveraging part of the base data by aligning the novel training instances to the closely related ones in the base training set. This expands the size of the effective novel training set by adding extra "related base" instances to the few novel ones, thereby allowing a constructive fine-tuning. We propose two associative
arXiv:1912.05094v3
fatcat:o7lxo6kvijbw3kjb6ekf6qytoa