Few-Shot Learning with Metric-Agnostic Conditional Embeddings
Learning high quality class representations from few examples is a key problem in metric-learning approaches to few-shot learning. To accomplish this, we introduce a novel architecture where class representations are conditioned for each few-shot trial based on a target image... (read more)
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