In this paper we propose a new few-shot learning approach that allows us to decouple the complexity of the task space from the complexity of individual tasks.
This paper is available on arxiv under CC 4.0 license. Authors: Andrey Zhmoginov, Google Research & {azhmogin,sandler,mxv}@google.com; Mark Sandler, Google Research & {azhmogin,sandler,mxv}@google.com; Max Vladymyrov, Google Research & {azhmogin,sandler,mxv}@google.com.
was replaced with a larger TIEREDIMAGENET dataset, we conduct many of our experiments and ablation studies using OMNIGLOT and TIEREDIMAGENET. Models. results presented in Table 1 were obtained with a simplified transformer model that had 1 layer, and did not have the final fully-connected layer and nonlinearity. This proved necessary for reducing model overfitting of this smaller dataset. Other model parameters are described in detail in Appendix B.
and TIEREDIMAGENET.
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