Few-shot Learning with Hyper-Parameters
A Baseline for Few-Shot Image Classification
We propose a metric that quantifies the"hardness"of a few-shot episode.We perform extensive studies on benchmark datasets to propose a metric that quantifies the"hardness"of a few-shot episode.This metric can be used to report the performance of few-shot algorithms in a more systematic way.We demonstrate the first few-shot learning results on the standard dataset of the imagenet-21k using a large number of meta-training classes.We find that using a large number of meta-training classes results in high few-shot accuracies even for a large number of few-shot classes.We find that using a large number of meta-training classes results in high few-shot accuracies even for a large number of few-shot classes.We perform extensive studies on benchmark datasets to propose a metric that quantifies the"hardness"of a few-shot episode.We propose a metric that quantifies the"hardness"of a few-shot episode.We perform extensive studies on benchmark datasets to propose a metric that quantifies the"hardness"of a few-shot episode.We find that using a large number of meta-training classes results in high few-shot accuracies even for a large number of few-shot classes.We perform extensive studies on benchmark datasets to propose a metric that quantifies the"hardness"of a few-shot episode.We perform extensive studies on benchmark datasets to propose a metric that quantifies the"hardness"of a few-shot episode.
