Instance-Level Embedding Adaptation for Few-Shot Learning

Instance-Level Embedding Adaptation for Few-Shot Learning

Blog Article

Few-shot learning aims to recognize novel categories from just a few labeled instances.Existing metric learning-based approaches perform classifications by nearest neighbor search in the embedding space.The embedding function is Paper Tableware a deep neural network and usually shared by all novel categories.

However, these brute approaches lack a fast adaptation mechanism like meta-learning when dealing with novel categories.To tackle this, we present a novel instance-level embedding adaptation mechanism, aiming at rapidly adapting embedding deep features to improve their generalization ability in recognizing novel categories.To this end, we design an Attention Adaptation Module to pull a query instance and its corresponding class center as close as possible.

Note that, each query instance is pulled closer to its corresponding class center before performing nearest neighbor classifications.This instance-level PRIMADOPHILUS OPTIMA reduction of intra-class distance increases the probability of correct classifications, and thus improves the generalization ability to embed deep features and promoting the performance.The extensive experiments are conducted on two benchmark datasets: miniImageNet and CUB.

Our approach yields very promising results on both datasets.In addition, in a realistic cross-domain evaluation setting, our method also achieves the-state-of-the-art performance.